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K-Means and Alternative Clustering Methods in Modern Power Systems

IEEE ACCESS (2023)

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A novel short-term load forecasting method based on mini-batch stochastic gradient descent regression model

Wu Lizhen et al.

Summary: This paper proposes an improved regression model based on mini-batch stochastic gradient descent, combined with big data analysis and processing platform, to accelerate load forecasting. An adaptive sorted neighborhood method and K-means clustering method are also introduced to clean up duplicate and noise data.

ELECTRIC POWER SYSTEMS RESEARCH (2022)

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Article Engineering, Electrical & Electronic

A novel optimization model for biding in the deregulated power market with pay as a bid settlement mechanism, based on the stochastic market clearing price

Omid Motamedisedeh et al.

Summary: This paper proposes an optimization-based bidding strategy for an electricity generation company in the day-ahead market. The proposed model takes into account the stochastic Market Clearing Price, historical data clustering using the K-Means Algorithm, and three types of cost functions. The results show that the proposed model can significantly improve the expected revenue.

ELECTRIC POWER SYSTEMS RESEARCH (2022)

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Review Energy & Fuels

Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review

Andrea Di Martino et al.

Summary: This paper provides a general overview of the modeling strategies adopted for evaluating or predicting energy consumption of electric vehicles, emphasizing the influence of basis of analysis and result types.

ENERGIES (2022)

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Article Engineering, Multidisciplinary

Short-Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid

Yuanyuan Liu

Summary: In order to improve the accuracy of ultra short-term power prediction of the photovoltaic power generation system, a short-term photovoltaic power prediction method based on an adaptive k-means and Gru machine learning model is proposed. The method determines the network structure and key parameters through experiments, and clusters the training set and the forecast data using adaptive k-means. The results show that the proposed method has better performance and robustness compared to traditional models.

MATHEMATICAL PROBLEMS IN ENGINEERING (2022)

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Article Chemistry, Analytical

A Multi-Stage Planning Method for Distribution Networks Based on ARIMA with Error Gradient Sampling for Source-Load Prediction

Sheng Yan et al.

Summary: This article proposes a multi-stage planning method for distribution networks based on source-load prediction, using ARIMA model and error gradient sampling to predict the scale of source-load development, providing research ideas and development references for the future evolution of smart distribution networks.

SENSORS (2022)

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Article Computer Science, Information Systems

A Decentralized Intrusion Detection System for Security of Generation Control

Siddhartha Deb Roy et al.

Summary: Several incidents of security breaches in the power system network have recently been reported, with limited emphasis on the impact of attacks on control signals. This article proposes a novel machine learning algorithm, CDEL, for detecting data anomalies and demonstrates its superiority through experimental results.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Article Energy & Fuels

Grid integration of electric vehicles for optimal marginal revenue of distribution system operator in spot market

Xiang Lei et al.

Summary: This paper discusses the importance of electric vehicles in reducing CO2 emissions and proposes an optimal strategy for distribution system operators to plan the charging and discharging of EVs. By flexibly dispatching charging services, both EVs and the grid can benefit from this approach.

ENERGY REPORTS (2022)

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Article Energy & Fuels

Multi-scene design analysis of integrated energy system based on feature extraction algorithm

Sihua Huang et al.

Summary: This paper proposes a load forecasting method based on feature clustering, which analyzes the correlation degree of control factors and the influence of environmental factors on loads, extracts feature vectors using convolutional neural networks, establishes clustering models for various energy loads, and obtains accurate load forecasting results.

ENERGY REPORTS (2022)

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Article Green & Sustainable Science & Technology

Frequency regulation control strategy of wind farms based on temporal and spatial uncertainty

Chen Changqing et al.

Summary: This study proposes a coordinated control strategy based on temporal and spatial uncertainties to optimize the overall frequency regulation capability of wind farms. Firstly, a spatial optimal grouping of DFIG in wind farms is achieved using model predictive control (MPC) and k-means clustering. Secondly, a timing coordinated control strategy is proposed based on the uncertainties of the DFIG operation state and wind speed. Finally, a coordinated control strategy based on temporal and spatial optimization is proposed to optimize the overall frequency regulation capability of wind farms.

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS (2022)

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Article Energy & Fuels

An unsupervised learning schema for seeking patterns in rms voltage variations at the sub-10-minute time scale

Younes Mohammadi et al.

Summary: This paper proposes an unsupervised learning schema for seeking patterns in rms voltage variations at the time scale of 1 s to 10 min. The proposed framework involves Kernel Principal Component Analysis (KPCA) and k-means clustering. The results show good separation of cluster centers, indicating the effectiveness of the schema. Additionally, the study reveals that existing statistical indices may not be sufficient to fully capture sub-10 min variations, emphasizing the need for pattern extraction along with statistics to quantify the voltage variations, levels, and patterns together.

SUSTAINABLE ENERGY GRIDS & NETWORKS (2022)

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Article Computer Science, Artificial Intelligence

Short-term power load forecasting based on gray relational analysis and support vector machine optimized by artificial bee colony algorithm

Xinfu Pang et al.

Summary: Short-term power load forecasting method based on critical influencing factors and screening of historically similar days, validated on actual load data in Nanjing, shows effectiveness.

PEERJ COMPUTER SCIENCE (2022)

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Article Energy & Fuels

Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking

Abigail Andrews et al.

Summary: This study proposes a four-step method for including grid interactivity and demand flexibility in building benchmarking models. It effectively clusters buildings and reveals patterns in building operations and demand flexibility issues.

APPLIED ENERGY (2022)

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Proceedings Paper Automation & Control Systems

Abnormal Wind Turbine Data Identification Using a Dirichlet Process Gaussian Mixture Model

Yu Gan et al.

Summary: This study proposes an abnormal data identification method based on DPGMM to preprocess raw data from wind turbine operation. By allocating data points into power bins and clustering them using DPGMM model, combined with confidence ellipses and data point distribution characteristics, abnormal data can be accurately identified.

2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC (2022)

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Proceedings Paper Computer Science, Theory & Methods

Agglomerative Hierarchical Clustering with Dynamic Time Warping for Household Load Curve Clustering

Fadi AlMahamid et al.

Summary: Energy companies implement demand response programs to match electricity demand and supply. This paper presents a methodology that combines Agglomerative Hierarchical Clustering with Dynamic Time Warping to classify residential households' daily load curves based on their consumption patterns. The results show that this approach outperforms other clustering algorithms in terms of clustering accuracy and the number of clusters required.

2022 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) (2022)

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Article Computer Science, Artificial Intelligence

M3W: Multistep Three-Way Clustering

Mingjing Du et al.

Summary: The article proposes a multistep three-way clustering algorithm (M3W) that adaptively acquires more information to capture the inherent clustering structure of the dataset using progressive erosion strategy and multistep three-way allocation strategy. Experimental results confirm the superior performance of the algorithm.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

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Article Energy & Fuels

Networked Microgrid Energy Management Based on Supervised and Unsupervised Learning Clustering

Navid Salehi et al.

Summary: Networked microgrid (NMG) is a novel conceptual paradigm that brings multiple advantages to the distributed system, including increased renewable energy utilization, improved system reliability and efficiency, and enhanced energy sharing flexibility. This paper proposes an energy management system that achieves a more efficient and reliable system through clustering and effective energy sharing.

ENERGIES (2022)

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Article Computer Science, Information Systems

Distributed-Swarm: A Real-Time Pattern Detection Model Based on Density Clustering

Tiao Qian et al.

Summary: This paper proposes a framework for frequent motion pattern detection of power data in a real-time distributed environment. By filtering the power data and introducing the concept of a historical state matrix, the detection speed and accuracy are improved.

IEEE ACCESS (2022)

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Article Energy & Fuels

A scenario-based two-stage stochastic optimization approach for multi-energy microgrids

Ke Li et al.

Summary: This paper proposes a two-stage stochastic optimization approach based on scenario analysis for the efficient operation of multi-energy microgrids. By fitting forecast errors using mixed distribution and conditional distribution, and reducing scenarios using an improved K-means clustering algorithm, the approach can flexibly deal with uncertainty in MEMG operations.

APPLIED ENERGY (2022)

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Article Computer Science, Information Systems

Incorporating Spatial and Temporal Correlations to Improve Aggregation of Decentralized Day-Ahead Wind Power Forecasts

Ndamulelo Mararakanye et al.

Summary: This study introduces a methodology to derive day-ahead aggregated point and probabilistic wind power forecasts from decentralized point forecasts of geographically distributed wind farms. The proposed methodology includes explanatory variables such as clustered decentralized point forecasts, hour of day and month of year, and atmospheric states derived from self-organizing maps. Testing on data from 29 wind farms in South Africa shows significant improvement compared to simply adding decentralized point forecasts.

IEEE ACCESS (2022)

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Article Business

How to conduct a bibliometric analysis: An overview and guidelines

Naveen Donthu et al.

Summary: Bibliometric analysis is a popular and rigorous method for exploring and analyzing large volumes of scientific data. While its application in business research is relatively new, this paper provides an overview of its methodology and step-by-step guidelines for conducting bibliometric analysis.

JOURNAL OF BUSINESS RESEARCH (2021)

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Article Energy & Fuels

A hybrid robust-stochastic framework for strategic scheduling of integrated wind farm and plug-in hybrid electric vehicle fleets

Saeed Zeynali et al.

Summary: This study focuses on the cooperative scheduling of integrated plug-in hybrid electric vehicle fleets and wind farm system in the day-ahead wholesale market, using a multi-objective two-stage bi-level hybrid stochastic-robust offering/bidding and scheduling strategy. The findings suggest that the proposed method can optimize social welfare while reducing emissions, and effectively manipulate locational marginal prices.

APPLIED ENERGY (2021)

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Article Energy & Fuels

Solar farm voltage anomaly detection using high-resolution μPMU data-driven unsupervised machine learning

Maitreyee Dey et al.

Summary: The integration of a micro-synchrophasor measurement unit with a power quality monitor provides analysts with high-resolution, high-precision, synchronized time-series data to improve solar farm performance and understand their impact on the distribution grid behavior. Machine learning methods can be utilized to process high-quality data for automatic fault detection, situational awareness, event forecasting, operational tuning, and condition-based maintenance planning in large solar photovoltaic facilities. The use of the Clustering Large Applications (CLARA) algorithm for event categorization and anomaly detection in empirical field data from utility-scale solar power generation sites in England demonstrated the potential of unsupervised machine learning approaches in this sector.

APPLIED ENERGY (2021)

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Article Energy & Fuels

A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting

Yinpeng Qu et al.

Summary: This paper presents a hybrid model based on Gated Recurrent Unit for forecasting distributed PV power generations. By utilizing locally historical PV power generation data, the model successfully predicts one-day-ahead solar power generation with improved accuracy.

APPLIED ENERGY (2021)

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Article Thermodynamics

Coordinated scheduling of generators and tie lines in multi-area power systems under wind energy uncertainty

Heng Zhang et al.

Summary: A novel stochastic MAUC framework is proposed to coordinate scheduling of generators and tie lines by reducing scenario numbers and establishing tie-line operation modes. Case studies show that more wind energy can be consumed through tie lines, coordinating the scheduling of generators and tie lines for improved efficiency and flexibility.

ENERGY (2021)

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Article Engineering, Electrical & Electronic

Comprehensive strategy for classification of voltage sags source location using optimal feature selection applied to support vector machine and ensemble techniques

Younes Mohammadi et al.

Summary: This paper introduces two classifiers, Support Vector Machine (SVM) and Ensemble, using genetic algorithm and 10-fold cross-validation to classify voltage sag sources, achieving high accuracy rates of 96.28% and 99.11% respectively. A comprehensive strategy to enhance SVM accuracy and maintain Ensemble performance by selecting appropriate features is presented, resulting in improved classification performance.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

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Article Energy & Fuels

A New Clustering Approach for Automatic Oscillographic Records Segmentation

Vitor Hugo Ferreira et al.

Summary: This paper presents a methodology to optimize the results of any clustering algorithm, regardless of the data spatial distribution. By evaluating the internal correlation of each cluster, the decision to proceed with a new partitioning round is determined. The results show that using the K-means algorithm with Silhouette and Davies-Bouldin validation indexes in a real waveform database yields consistent results.

ENERGIES (2021)

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Article Chemistry, Physical

Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution

Qiao Xue et al.

Summary: A novel fault diagnosis and abnormality detection method for battery packs of electric scooters is proposed in this study, utilizing statistical distribution and parameter variation to determine operation states, employing algorithms and screening methods to detect abnormal cells, and identifying fault types and locating faulty cells by calculating fault frequency.

JOURNAL OF POWER SOURCES (2021)

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Article Computer Science, Information Systems

Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms

Erich Schubert et al.

Summary: The authors discuss the challenges of clustering non-Euclidean data and introduce the popular Partitioning Around Medoids (PAM) algorithm, along with modifications to accelerate its runtime without compromising results. By achieving significant speedups, PAM becomes applicable to larger datasets and higher k values, which is crucial for various domains and applications.

INFORMATION SYSTEMS (2021)

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Article Computer Science, Interdisciplinary Applications

Clustering of graphs using pseudo-guided random walk

Zahid Halim et al.

Summary: This paper introduces a random walk-based method to cluster graphs, which uses information of nodes and edges to guide the random walk process and achieve clustering by finding weighted edges and neighboring nodes. Experimental results suggest better performance of this method on evaluation metrics across 18 real-world benchmark datasets.

JOURNAL OF COMPUTATIONAL SCIENCE (2021)

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Article Energy & Fuels

Optimal allocation of onshore wind power in China based on cluster analysis

Chongyu Zhang et al.

Summary: This study classifies China's mainland into seven wind zones and identifies northern China and southeastern regions as key areas for future wind power deployment. Through optimization modeling, the quality of wind power is expected to be further improved.

APPLIED ENERGY (2021)

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Article Computer Science, Interdisciplinary Applications

Anomaly detection method based on the deep knowledge behind behavior patterns in industrial components. Application to a hydropower plant

Pablo Calvo-Bascones et al.

Summary: This study introduces a novel approach to anomaly detection and diagnosis of industrial component behaviors by creating behavior patterns using unsupervised machine learning algorithms, and enhancing pattern characterization through local Probability Density Distribution algorithm. This method allows for the surveillance of dynamic behaviors and degradation processes in real world applications.

COMPUTERS IN INDUSTRY (2021)

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Review Engineering, Electrical & Electronic

Review of preprocessing methods for univariate volatile time-series in power system applications

Kumar Gaurav Ranjan et al.

Summary: This paper examines and categorizes preprocessing methods for time-series data, evaluating and comparing the capabilities of each method. The application of these methods to commonly used time-series data in power systems is discussed, along with the impact on method performance and potential for improvement.

ELECTRIC POWER SYSTEMS RESEARCH (2021)

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Article Energy & Fuels

Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions

Seyed Mandi Miraftabzadeh et al.

Summary: This study proposes a model to calculate the total energy consumption of all EVs in a city within a big data regime, successfully applied to real-world datasets and finding that energy consumption is highly correlated with weekday traffic flow.

ENERGIES (2021)

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Article Energy & Fuels

Determination of Price Zones during Transition from Uniform to Zonal Electricity Market: A Case Study for Turkey

Gokturk Poyrazoglu

Summary: Different pricing models can increase market competitiveness in the electricity market. Different electricity systems use different market structures. Determining price zones is crucial for achieving a competitive market that generates accurate price signals.

ENERGIES (2021)

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Article Engineering, Multidisciplinary

Dynamic Equivalent Modeling of Wind Farm Based on Dominant Variable Hierarchical Clustering Algorithm

Wenbo Jiang et al.

Summary: A dynamic equivalent modeling method based on principal component analysis and hierarchical clustering algorithm is proposed, which accurately describes the actual operating state of the wind turbine group and has good application prospects for characteristics analysis of large-scale wind farms.

MATHEMATICAL PROBLEMS IN ENGINEERING (2021)

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Article Computer Science, Artificial Intelligence

Anomaly detection in multivariate streaming PMU data using density estimation technique in wide area monitoring system

A. L. Amutha et al.

Summary: This study proposes a novel framework based on density estimation technique for detecting anomalies in multivariate streaming PMU data, which is suitable for any type of anomaly detection and allows for real-time analysis of streaming data, online monitoring of abnormalities, and early intervention in PMU data.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Engineering, Electrical & Electronic

Spectral clustering based demand-oriented representative days selection method for power system expansion planning

Shengpu Gao et al.

Summary: This paper proposes a spectral clustering based demand-oriented representative days selection method, which extracts feature vectors and partitions graphs using spectral clustering to accurately capture the demand for adequacy and flexibility in system scheduling. The superior performance of SCDOR is demonstrated through case studies on the Texas power system and IEEE RTS-79 system with different levels of modeling complexity.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence

Research of power load prediction based on boost clustering

Junde Chen et al.

Summary: Power load prediction is crucial for energy management in power systems. In this study, a boost clustering-based approach is proposed to enhance the traditional k-means algorithm and predict power load by clustering users and summing up the predicted results. Experimental results show the effectiveness of this approach over direct prediction methods.

SOFT COMPUTING (2021)

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Article Computer Science, Information Systems

High energy efficient lifetime management system and trust management framework for manet using self-configurable cluster mechanism

Gopala C. Krishnan et al.

Summary: In Mobile Ad Hoc Networks, cluster approaches divide nodes into small groups to save energy, but Cluster Heads may fail due to energy instability. A self-configurable cluster mechanism is proposed to detect and replace unstable Cluster Heads, reducing energy consumption.

PEER-TO-PEER NETWORKING AND APPLICATIONS (2021)

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Review Environmental Sciences

Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis

S. Mohammad Mirmazloumi et al.

Summary: This study evaluates the status and trends of wetland studies in Canada using Remote Sensing (RS) technology by reviewing scientific papers published between 1976 and 2020. The analysis shows a rising trend in utilizing multi-source RS datasets and advanced machine learning algorithms for wetland mapping in Canada, with most studies focusing on the province of Ontario. Additionally, pixel-based supervised classifiers were found to be the most popular wetland classification algorithms.

REMOTE SENSING (2021)

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Article Thermodynamics

A novel microgrid support management system based on stochastic mixed-integer linear programming

I. L. R. Gomes et al.

Summary: This paper presents a microgrid support management system based on a stochastic mixed-integer linear programming problem, managed and operated by a new electricity market agent, the microgrid aggregator. Plausible scenarios are computed using Kernel Density Estimation to characterize random variables, and a scenario reduction is carried out with a two-tier procedure involving K-means clustering technique and a fast backward scenario reduction method. Case studies demonstrate the performance of the microgrid and validate the methodology proposed for the microgrid support management system.

ENERGY (2021)

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Article Thermodynamics

Improving energy self-sufficiency of a renovated residential neighborhood with heat pumps by analyzing smart meter data

Shalika Walker et al.

Summary: This study discusses the improvement of energy self-sufficiency through data-driven clustering, prediction, and energy management strategies, combined with smart grid applications and electric energy storage technology, which can achieve a significant increase in energy self-sufficiency.

ENERGY (2021)

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Article Engineering, Electrical & Electronic

Cross-Layer Resource Allocation for UAV-Assisted Wireless Caching Networks With NOMA

Yue Yin et al.

Summary: Unmanned aerial vehicle (UAV) assisted wireless caching networks (WCN) have been proposed as a promising approach in 6G communication systems to reduce network load and improve energy efficiency. To enhance spectrum efficiency and system capacity, NOMA is utilized in UAV-assisted WCN for serving multiple users on the same spectrum simultaneously, along with a cross-layer resource allocation strategy including UAV scheduling, user grouping, and power allocation. The research proposes.-K-means algorithm for user clustering and UAV deployment, SQF power allocation method considering statistic QoS, and IQA strategy based on instantaneous QoS to reduce file outage probability. Additionally, an improved CLO power allocation strategy is introduced to maximize system hit probability.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

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Article Energy & Fuels

Research on key node identification scheme for power system considering malicious data attacks

Yucheng Ding et al.

Summary: This paper proposes an effective defensive scheme against false data injection attacks, studying the impact of attacks on node stability indices and clustering nodes based on the results, defining vulnerability levels and taking proactive defense measures. Simulation results demonstrate the feasibility and effectiveness of the method.

ENERGY REPORTS (2021)

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Article Green & Sustainable Science & Technology

An online driver behavior adaptive shift strategy for two-speed AMT electric vehicle based on dynamic corrected factor

Xinyou Lin et al.

Summary: The proposed shift strategy based on dynamic corrected factors effectively reduces energy consumption in electric vehicles while meeting the needs of various driver styles.

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS (2021)

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Article Energy & Fuels

Research on energy management and control method of microgrid considering health status of batteries in echelon utilization

Ning Yan et al.

Energy Reports (2021)

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Article Energy & Fuels

A data-mining based optimal demand response program for smart home with energy storages and electric vehicles

Masoud Babaei et al.

Summary: Modern appliances with high electricity demand have significant impact on residential energy consumption, but face environmental concerns and high bills. The proposed load management framework aims to alleviate these issues by utilizing controllable appliances, load models, and energy storage systems, with optimization achieved through data mining and scheduling algorithms.

JOURNAL OF ENERGY STORAGE (2021)

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Article Green & Sustainable Science & Technology

Optimal stochastic scenario-based allocation of smart grids' renewable and non-renewable distributed generation units and protective devices

Mohammad-Reza Yaghoubi-Nia et al.

Summary: The paper proposes a stochastic scenario-based reliability evaluation method for optimal allocation of smart grids' PDs and DGs. The method applies scenario reduction using the k-means algorithm and focuses on PDs malfunction, showing 1.2% more precision than current analytical methods.

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS (2021)

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Article Computer Science, Artificial Intelligence

Risk Assessment Algorithm for Power Transformer Fleets Based on Condition and Strategic Importance

Diego A. Zaldivar et al.

Summary: This paper proposes a new methodology to assess the risk of power transformer fleets, taking into account both technical condition and strategic importance. By evaluating units using a health index and importance index, similar units are clustered to assist asset managers in maintenance decision-making.

ALGORITHMS (2021)

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Article Computer Science, Hardware & Architecture

Synergism of synchrophasor measurements and data analytics for enhancing situational awareness of power grid

Amit R. Kulkarni et al.

Summary: This study demonstrates the real-world experience of integrating multi-location high-granularity synchrophasor measurements and various data analysis techniques for disturbance analysis and enhancing power grid situational awareness. It shows the importance of correlation techniques and clustering techniques in understanding grid behavior and highlights the application of these techniques for system operators in managing the grid effectively.

COMPUTERS & ELECTRICAL ENGINEERING (2021)

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Article Energy & Fuels

Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning

Hanaa Talei et al.

Summary: This study focuses on the impact of Houston's climate on HVAC systems in buildings, proposing energy-saving strategies based on data clustering. Through unsupervised learning techniques and cluster analysis, building managers can identify and utilize potential energy-saving opportunities.

ENERGIES (2021)

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Article Energy & Fuels

Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Seyed Mahdi Miraftabzadeh et al.

Summary: With recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities, new research opportunities are emerging for applying Machine Learning to improve the observability and efficiency of modern power grids. A systematic review of state-of-the-art studies implementing ML techniques in power systems reveals that ML algorithms can handle massive quantities of data with high dimensionality and hybrid models generally show better performances than single ML-based models.

ENERGIES (2021)

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Article Energy & Fuels

An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings

Anam-Nawaz Khan et al.

Summary: A spatial and temporal ensemble forecasting model is proposed for short-term electric consumption forecasting in residential buildings, utilizing deep learning algorithms at different spatial scales. Experimental results demonstrate superior performance of the proposed model compared to other machine learning and deep learning variants, achieving lower forecasting errors at building and floor levels.

ENERGIES (2021)

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Article Energy & Fuels

An Overview of Probabilistic Dimensioning of Frequency Restoration Reserves with a Focus on the Greek Electricity Market

Anthony Papavasiliou

Summary: This article compares dynamic and static dimensioning of frequency restoration reserves based on probabilistic criteria, finding potential benefits of dynamic dimensioning for the Greek electricity market, such as a reduction in average reserve requirements and the preservation of a constant risk profile due to the adaptive nature of probabilistic dimensioning.

ENERGIES (2021)

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Article Thermodynamics

Wind turbine power output prediction using a new hybrid neuro-evolutionary method

Mehdi Neshat et al.

Summary: This study introduces a novel hybrid deep learning evolutionary approach for accurate wind turbine farm power output prediction. The approach is divided into three stages including preprocessing, decomposition, and an alternating optimization algorithm based on prior knowledge. Experimental results demonstrate the superiority of the method in accurate prediction and computational runtime.

ENERGY (2021)

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Article Automation & Control Systems

A Novel k-Means Clustering and Weighted k-NN-Regression-Based Fast Transmission Line Protection

Amit Kumar Gangwar et al.

Summary: This article introduces an algorithm for protecting transmission lines, which detects and locates faults using k-means clustering and weighted k-nearest neighbor (k-NN) regression. The algorithm synchronizes and samples three-phase current signals, computes cumulative differential sum (CDS), and uses various case studies to validate its robustness.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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Article Engineering, Electrical & Electronic

Data clustering based probabilistic optimal scheduling of an energy hub considering risk-averse

Yousef Allahvirdizadeh et al.

Summary: This paper presents a risk-constrained stochastic scheduling method for an energy hub, considering uncertainties of renewable generation and load demands. By managing uncertainties in input random variables using an efficient data clustering method, the method aims to reduce operational costs and risk costs of the energy hub. Simulation results demonstrate significant reductions in operational costs and improvements in risk costs with the integration of various technologies and demand response programs.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

MILP model for volt-var optimization considering chronological operation of distribution systems containing DERs

Bibiana P. Ferraz et al.

Summary: This paper introduces a mixed-integer linear programming model for volt-var optimization in distribution systems with distributed energy resources (DERs). The model effectively controls the operation of capacitor banks and voltage regulators based on steady-state operation within typical scenarios. The study shows that the new model maintains good agreement between different loads and DERs in terms of simultaneity and chronological combination.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

Enhanced performance Gaussian process regression for probabilistic short-term solar output forecast

Fatemeh Najibi et al.

Summary: This paper proposes a novel probabilistic framework to predict short-term PV output, taking into account the variability of weather data over different seasons. By using feature selection, clustering, and Gaussian Process Regression, a function relating selected features with solar output is established. Testing on five solar generation plants shows that the proposed method significantly reduces errors compared to existing methodologies.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

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Article Green & Sustainable Science & Technology

Apriori and K-Means algorithms of machine learning for spatio-temporal solar generation balancing

Nurseda Y. Yurusen et al.

Summary: This study analyzed hourly simulation data using machine learning algorithms and association rules to address spatio-temporal operational balancing constraints for solar PV. The proposed model serves as a fast and effective decision-making tool for system operators with minimal expert knowledge and can be integrated into optimal power flow analysis constraints.

RENEWABLE ENERGY (2021)

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Article Engineering, Electrical & Electronic

Unsupervised machine learning techniques applied to composite reliability assessment of power systems

Fernando A. Assis et al.

Summary: This paper proposes a new method to evaluate the composite reliability of electrical power networks efficiently through nonsequential Monte Carlo simulation and unsupervised machine learning techniques. The proposed approach not only significantly reduces computational effort, but also maintains the accuracy of estimated indices. The results demonstrate the good performance of the method on different power systems.

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

Electric vehicle charging current scenario generation based on generative adversarial network combined with clustering algorithm

Fan Yang et al.

Summary: The paper introduces a data-driven approach using generative adversarial networks (GANs) to generate scenarios of electric vehicle (EV) charging, which can learn the distribution of EV charging current and obtain richer scenarios. The method divides the charging current distribution into four areas using K-Means clustering algorithm, and leverages GANs with gradient penalty (GP) for faster training and optimization of the Lipschitz limit. Statistical methods are then used to estimate the quality of the generated data, demonstrating the effectiveness of the proposed method for extending historical data for future EV operation and planning compared to traditional GANs.

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS (2021)

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Article Energy & Fuels

Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Honglu Zhu et al.

Summary: This study proposes a method for configuring energy storage capacity based on the uncertainty of PV power generation. By analyzing power forecast errors in different weather types, the optimized energy storage configuration of a PV plant can be achieved.

GLOBAL ENERGY INTERCONNECTION-CHINA (2021)

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Article Computer Science, Information Systems

Bibliometric Analysis of the Blockchain Scientific Evolution: 2014-2020

Jianli Luo et al.

Summary: This study examines the scientific evolution and research focus of Blockchain through bibliometric analysis, identifying key developments in the field and summarizing research hotspots in different periods. The findings provide meaningful references for scholars to identify research hotspots and future research directions in the Blockchain field.

IEEE ACCESS (2021)

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Article Computer Science, Information Systems

Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach

Marco Savi et al.

Summary: This paper proposes an innovative architecture with Federated Learning and Edge Computing capabilities, allowing users to locally train LSTM models and aggregate them through a specific-purpose node for improved edge forecasting. By considering relevant features and grouping users based on consumption similarities or socioeconomic affinities, the approach avoids the privacy and scalability issues of centrally collecting sensitive data from users.

IEEE ACCESS (2021)

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Article Computer Science, Information Systems

Harmonic distortion characterization in groups of distribution networks applying the IEEE Standard 519-2014

Lester Marrero et al.

Summary: This paper investigates the similarities in harmonic distortion behavior in distribution networks using k-means clustering and rough sets theory. The empirical analysis demonstrates the effectiveness of this method in characterizing and validating the behavior of distribution networks.

IEEE LATIN AMERICA TRANSACTIONS (2021)

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Article Computer Science, Information Systems

A Hybrid Short-Term Load Forecasting Model Based on Improved Fuzzy C-Means Clustering, Random Forest and Deep Neural Networks

Fu Liu et al.

Summary: This paper proposes a novel STLF model based on an improved FCM algorithm, RF, and DNN, which successfully improves the accuracy of load consumption data prediction. Experimental results demonstrate that the model outperforms other methods in terms of prediction performance and significantly enhances the prediction accuracy of load consumption data on holidays.

IEEE ACCESS (2021)

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Article Energy & Fuels

Short-term forecasting of individual residential load based on deep learning and K-means clustering

Fujia Han et al.

Summary: A short-term individual residential load forecasting method based on a combination of deep learning and k-means clustering is proposed in this paper, which can effectively extract similarity and accurately predict residential load. Experimental results demonstrate that this method achieves higher prediction accuracy compared to other methods.

CSEE JOURNAL OF POWER AND ENERGY SYSTEMS (2021)

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Article Computer Science, Information Systems

A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on Clustering

Khursheed Aurangzeb et al.

Summary: With advancements in renewable energy sources, AMI, and communication technologies, traditional control networks are evolving towards smart grids. Short-term load forecasting for individual and similar energy customers is crucial, but challenging due to high volatility and uncertainty. Several machine/deep learning models have been developed, but training a model for each customer is not practical. A CNN model in pyramid architecture is proposed for effective load forecasting for similar energy-profile customers, resulting in improved forecasting results.

IEEE ACCESS (2021)

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Review Green & Sustainable Science & Technology

Industry 4.0 deployment in the construction industry: a bibliometric literature review and UK-based case study

Chris Newman et al.

Summary: Industry 4.0 is predicted to revolutionize commercial and manufacturing practices through improved knowledge utilization, but the construction industry has been slow to adopt innovations. Research shows that Industry 4.0 is relatively new, with developed countries and Germany leading in the field.

SMART AND SUSTAINABLE BUILT ENVIRONMENT (2021)

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Review Environmental Sciences

Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980-2019)

P. Govender et al.

ATMOSPHERIC POLLUTION RESEARCH (2020)

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Article Energy & Fuels

The EV-olution of the power system: A spatio-temporal optimisation model to investigate the impact of electric vehicle deployment

Clara F. Heuberger et al.

APPLIED ENERGY (2020)

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A CoMP-Based outage compensation solution for heterogeneous femtocell networks

Yu Jie et al.

COMPUTER NETWORKS (2020)

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Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems

Sunme Park et al.

ENERGIES (2020)

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Article Economics

Assessing the willingness of residential electricity consumers to adopt demand side management and distributed energy resources: A case study on the Japanese market

Nikolaos Iliopoulos et al.

ENERGY POLICY (2020)

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Article Engineering, Electrical & Electronic

An Energy Prediction Approach for a Nonintrusive Load Monitoring in Home Appliances

Bundit Buddhahai et al.

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS (2020)

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Article Engineering, Electrical & Electronic

Statistical Characterization of PMU Error for Robust WAMS Based Analytics

Tabia Ahmad et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2020)

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Article Engineering, Electrical & Electronic

Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting

Mingyang Sun et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2020)

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Article Engineering, Electrical & Electronic

Dynamic Aggregation of Grid-Tied Three-Phase Inverters

Victor Purba et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2020)

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Article Green & Sustainable Science & Technology

A comparative study of clustering techniques for electrical load pattern segmentation

Amin Rajabi et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)

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Article Green & Sustainable Science & Technology

A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning

Gang Wang et al.

RENEWABLE ENERGY (2020)

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Article Chemistry, Analytical

Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data

Amin Ullah et al.

SENSORS (2020)

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Article Telecommunications

Machine Learning Approach Electric Appliance Consumption and Peak Demand Forecasting of Residential Customers Using Smart Meter Data

Fikirte Zemene Abera et al.

WIRELESS PERSONAL COMMUNICATIONS (2020)

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Article Biochemistry & Molecular Biology

SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues

Carly G. K. Ziegler et al.

CELL (2020)

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Article Green & Sustainable Science & Technology

Extreme Photovoltaic Power Analytics for Electric Utilities

Zefan Tang et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2020)

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Article Energy & Fuels

Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

Hyun Cheol Jeong et al.

ENERGIES (2020)

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Article Energy & Fuels

Barrier Analysis for the Deployment of Renewable-Based Mini-Grids in Myanmar Using the Analytic Hierarchy Process (AHP)

Masako Numata et al.

ENERGIES (2020)

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Article Engineering, Multidisciplinary

Multiobjective Framework for Optimal Integration of Solar Energy Source in Three-Phase Unbalanced Distribution Network

Sriparna Roy Ghatak et al.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2020)

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Article Engineering, Electrical & Electronic

A Novel Two-Stage Multi-Layer Constrained Spectral Clustering Strategy for Intentional Islanding of Power Grids

Morteza Dabbaghjamanesh et al.

IEEE TRANSACTIONS ON POWER DELIVERY (2020)

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Article Engineering, Electrical & Electronic

Research on state evaluation and risk assessment for relay protection system based on machine learning algorithm

Liming Ying et al.

IET GENERATION TRANSMISSION & DISTRIBUTION (2020)

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Article Green & Sustainable Science & Technology

Wind turbine power output very short-term forecast: A comparative study of data clustering techniques in a PSO-ANFIS model

Paul A. Adedeji et al.

JOURNAL OF CLEANER PRODUCTION (2020)

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Article Green & Sustainable Science & Technology

Hierarchical planning of PEV charging facilities and DGs under transportation-power network couplings

Siyang Sun et al.

RENEWABLE ENERGY (2020)

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Article Biology

Swarm intelligence based clustering technique for automated lesion detection and diagnosis of psoriasis

Manoranjan Dash et al.

COMPUTATIONAL BIOLOGY AND CHEMISTRY (2020)

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Article Energy & Fuels

Machine learning driven smart electric power systems: Current trends and new perspectives

Muhammad Sohail Ibrahim et al.

APPLIED ENERGY (2020)

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Article Computer Science, Artificial Intelligence

Border-Peeling Clustering

Hadar Averbuch-Elor et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)

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Article Engineering, Electrical & Electronic

Impact of harmonic limits on PV penetration levels in unbalanced distribution networks considering load and irradiance uncertainty

Ibrahim Cagri Barutcu et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2020)

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Review Computer Science, Information Systems

The k-means Algorithm: A Comprehensive Survey and Performance Evaluation

Mohiuddin Ahmed et al.

ELECTRONICS (2020)

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Article Energy & Fuels

An empirical analysis of domestic electricity load profiles: Who consumes how much and when?

Gianluca Trotta

APPLIED ENERGY (2020)

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Article Computer Science, Hardware & Architecture

Deep Reinforcement Learning Aided Cell Outage Compensation Framework in 5G Cloud Radio Access Networks

Peng Yu et al.

MOBILE NETWORKS & APPLICATIONS (2020)

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Article Energy & Fuels

Optimization of Voltage Unbalance Compensation by Smart Inverter

Ryuto Shigenobu et al.

ENERGIES (2020)

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Article Thermodynamics

Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization

Yonggang Liu et al.

ENERGY (2020)

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Article Construction & Building Technology

Generating realistic building electrical load profiles through the Generative Adversarial Network (GAN)

Zhe Wang et al.

ENERGY AND BUILDINGS (2020)

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Applying multiple types of demand response to optimal day-ahead stochastic scheduling in the distribution network

Jie Yang et al.

IET GENERATION TRANSMISSION & DISTRIBUTION (2020)

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Article Engineering, Electrical & Electronic

Maximum frequency deviation assessment with clustering based on metric learning

Huarui Li et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2020)

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Article Computer Science, Artificial Intelligence

Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection

Junwen Chen et al.

KNOWLEDGE-BASED SYSTEMS (2020)

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Article Computer Science, Artificial Intelligence

Deep k-Means: Jointly clustering with k-Means and learning representations

Maziar Moradi Fard et al.

PATTERN RECOGNITION LETTERS (2020)

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Article Energy & Fuels

Economic battery sizing and power dispatch in a grid-connected charging station using convex method

Peiman Mirhoseini et al.

JOURNAL OF ENERGY STORAGE (2020)

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Article Computer Science, Interdisciplinary Applications

Novel electricity pattern identification system based on improved I-nice algorithm

Yu-Lin He et al.

COMPUTERS & INDUSTRIAL ENGINEERING (2020)

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Article Thermodynamics

Short-term wind power forecasting approach based on Seq2Seq model using NWP data

Yu Zhang et al.

ENERGY (2020)

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Article Automation & Control Systems

Time Series Data-Driven Batch Assessment of Power System Short-Term Voltage Security

Lipeng Zhu et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Automation & Control Systems

Fault Location in Smart Grids Through Multicriteria Analysis of Group Decision Support Systems

Hossein Hassani et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Energy & Fuels

Comprehensive stochastic optimal scheduling in residential micro energy grid considering pumped-storage unit and demand response

Yizhuo Ma et al.

JOURNAL OF ENERGY STORAGE (2020)

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Proceedings Paper Computer Science, Artificial Intelligence

Cluster Quality Analysis Using Silhouette Score

Ketan Rajshekhar Shahapure et al.

2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020) (2020)

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Article Engineering, Electrical & Electronic

Diagnosis of the single phase-to-ground fault in distribution network based on feature extraction and transformation from the waveforms

Fang Shi et al.

IET GENERATION TRANSMISSION & DISTRIBUTION (2020)

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Article Engineering, Electrical & Electronic

Plug-in Electric Vehicle Behavior Modeling in Energy Market: A Novel Deep Learning-Based Approach With Clustering Technique

Hamidreza Jahangir et al.

IEEE TRANSACTIONS ON SMART GRID (2020)

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Article Engineering, Electrical & Electronic

Data-driven Operation Risk Assessment of Wind-integrated Power Systems via Mixture Models and Importance Sampling

Osama Aslam Ansari et al.

JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY (2020)

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Article Engineering, Manufacturing

Pattern Recognition in Multivariate Time Series: Towards an Automated Event Detection Method for Smart Manufacturing Systems

Vadim Kapp et al.

JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING (2020)

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Article Energy & Fuels

Integrated forward/reverse logistics thin-film photovoltaic power plant supply chain network design with uncertain data

Vahid Shahbazbegian et al.

APPLIED ENERGY (2020)

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Article Computer Science, Information Systems

Dynamic Equivalent Modeling for Wind Farms With DFIGs Using the Artificial Bee Colony With K-Means Algorithm

Xiaohui Wang et al.

IEEE ACCESS (2020)

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Article Engineering, Multidisciplinary

Deep Learning Based Surface Irradiance Mapping Model for Solar PV Power Forecasting Using Sky Image

Zhao Zhen et al.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2020)

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Proceedings Paper Automation & Control Systems

Optimal Adaptive Protection Using Setting Groups Allocation Based on Impedance Matrix

Ali Abbasi et al.

2020 14TH INTERNATIONAL CONFERENCE ON PROTECTION AND AUTOMATION OF POWER SYSTEMS (IPAPS) (2020)

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Article Computer Science, Theory & Methods

MSGC: Multi-scale grid clustering by fusing analytical granularity and visual cognition for detecting hierarchical spatial patterns

Zhipeng Gui et al.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2020)

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Article Telecommunications

Ensemble Learning for Load Forecasting

Lingxiao Wang et al.

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING (2020)

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Article Computer Science, Information Systems

Unsupervised K-Means Clustering Algorithm

Kristina P. Sinaga et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Scenario-Set-Based Economic Dispatch of Power System With Wind Power and Energy Storage System

Yuan Zeng et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Optimize Grouping and Path of Pylon Inspection in Power System

Zhao-Long Hu et al.

IEEE ACCESS (2020)

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Article Computer Science, Theory & Methods

TURNING BIG DATA INTO TINY DATA: CONSTANT-SIZE CORESETS FOR k-MEANS, PCA, AND PROJECTIVE CLUSTERING

Dan Feldman et al.

SIAM JOURNAL ON COMPUTING (2020)

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Article Computer Science, Information Systems

Clustered Hybrid Wind Power Prediction Model Based on ARMA, PSO-SVM, and Clustering Methods

Yurong Wang et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Discovery and Prediction of Stock Index Pattern via Three-Stage Architecture of TICC, TPA-LSTM and Multivariate LSTM-FCNs

Hongbing Ouyang et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Household Power Consumption Prediction Method Based on Selective Ensemble Learning

Kun Liang et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Load forecasting for smart grid using non-linear model in Hadoop distributed file system

S. Arun Jees et al.

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2019)

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Article Computer Science, Hardware & Architecture

Fairness in Real-Time Energy Pricing for Smart Grid Using Unsupervised Learning

Hafiz Tayyeb Javed et al.

COMPUTER JOURNAL (2019)

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Article Engineering, Electrical & Electronic

Data-Driven Multi-Hidden Markov Model-Based Power Quality Disturbance Prediction That Incorporates Weather Conditions

Fei Xiao et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2019)

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Article Computer Science, Artificial Intelligence

A two-stage short-term load forecasting approach using temperature daily profiles estimation

Kheir Eddine Farfar et al.

NEURAL COMPUTING & APPLICATIONS (2019)

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Article Engineering, Civil

Joint Clustering and Power Allocation for the Cross Roads Congestion Scenarios in Cooperative Vehicular Networks

Hailin Xiao et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)

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Article Engineering, Electrical & Electronic

A cluster-based optimization approach to support the participation of an aggregator of a larger number of prosumers in the day-ahead energy market

Jose Iria et al.

ELECTRIC POWER SYSTEMS RESEARCH (2019)

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Article Energy & Fuels

Optimal Scheduling of an Isolated Wind-Diesel-Energy Storage System Considering Fast Frequency Response and Forecast Error

Nhung Nguyen Hong et al.

ENERGIES (2019)

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Article Engineering, Electrical & Electronic

Daily pattern prediction based classification modeling approach for day-ahead electricity price forecasting

Fei Wang et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2019)

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Article Engineering, Electrical & Electronic

Transmission Expansion and Reactive Power Planning Considering Wind Energy Investment Using A Linearized AC Model

Abolfazi Arabpour et al.

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (2019)

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Article Computer Science, Artificial Intelligence

Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption

Peter Laurinec et al.

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS (2019)

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Article Energy & Fuels

Time series clustering and physical implication for photovoltaic array systems with unknown working conditions

Guangyu Liu et al.

SOLAR ENERGY (2019)

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Article Engineering, Electrical & Electronic

Probabilistic load flow method considering large-scale wind power integration

Xiaoyang Deng et al.

JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY (2019)

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Article Energy & Fuels

A data mining-driven incentive-based demand response scheme for a virtual power plant

Zhe Luo et al.

APPLIED ENERGY (2019)

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Article Energy & Fuels

Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model

Jinhua Zhang et al.

APPLIED ENERGY (2019)

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Article Energy & Fuels

Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response

Catia Silva et al.

ENERGIES (2019)

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Article Energy & Fuels

Online Building Load Management Control with Plugged-in Electric Vehicles Considering Uncertainties

Moses Amoasi Acquah et al.

ENERGIES (2019)

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Article Computer Science, Theory & Methods

LSTM-EFG for wind power forecasting based on sequential correlation features

Ruiguo Yu et al.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2019)

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Article Engineering, Electrical & Electronic

A Data-Driven Bottom-Up Approach for Spatial and Temporal Electric Load Forecasting

Chengjin Ye et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2019)

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Article Mathematics, Interdisciplinary Applications

Comparison of Similarity Measures for Categorical Data in Hierarchical Clustering

Zdenek Sulc et al.

JOURNAL OF CLASSIFICATION (2019)

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Article Computer Science, Artificial Intelligence

Islanding fault detection based on data-driven approach with active developed reactive power variation

Yang Li et al.

NEUROCOMPUTING (2019)

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Article Chemistry, Multidisciplinary

Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

Jaehyun Lee et al.

APPLIED SCIENCES-BASEL (2019)

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Article Computer Science, Information Systems

LASSO and LSTM Integrated Temporal Model for Short-Term Solar Intensity Forecasting

Yu Wang et al.

IEEE INTERNET OF THINGS JOURNAL (2019)

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Article Engineering, Electrical & Electronic

Optimal location-allocation of storage devices and renewable-based DG in distribution systems

Juan M. Home-Ortiz et al.

ELECTRIC POWER SYSTEMS RESEARCH (2019)

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Article Engineering, Electrical & Electronic

Strategic participation in competitive electricity markets: Internal versus sectorial data analysis

Tiago Pinto et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2019)

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Article Computer Science, Information Systems

Identification of Vulnerable Lines in Smart Grid Systems Based on Affinity Propagation Clustering

Qinghe Gao et al.

IEEE INTERNET OF THINGS JOURNAL (2019)

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A Comprehensive Approach for the Clustering of Similar-Performance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles

Wei Li et al.

ENGINEERING (2019)

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Article Thermodynamics

Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management

S. Yilmaz et al.

ENERGY (2019)

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Article Engineering, Electrical & Electronic

Dynamic Fault Prediction of Power Transformers Based on Hidden Markov Model of Dissolved Gases Analysis

Jun Jiang et al.

IEEE TRANSACTIONS ON POWER DELIVERY (2019)

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How much can k-means be improved by using better initialization and repeats?

Pasi Franti et al.

PATTERN RECOGNITION (2019)

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Machine Learning-Based Anomaly Detection for Load Forecasting Under Cyberattacks

Mingjian Cui et al.

IEEE TRANSACTIONS ON SMART GRID (2019)

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Article Energy & Fuels

Residential battery sizing model using net meter energy data clustering

Rui Tang et al.

APPLIED ENERGY (2019)

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Article Thermodynamics

Random forest forest solar power forecast based on classification optimization

Da Liu et al.

ENERGY (2019)

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Article Thermodynamics

A novel two-stage deep learning wind speed forecasting method with adaptive multiple error corrections and bivariate Dirichlet process mixture model

Hui Liu et al.

ENERGY CONVERSION AND MANAGEMENT (2019)

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Residential Power Forecasting Using Load Identification and Graph Spectral Clustering

Chinthaka Dinesh et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS (2019)

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Bottom-Up Load Forecasting With Markov-Based Error Reduction Method for Aggregated Domestic Electric Water Heaters

Xun Gong et al.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2019)

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Article Engineering, Electrical & Electronic

Combining Unsupervised and Supervised Learning for Asset Class Failure Prediction in Power Systems

Ming Dong

IEEE TRANSACTIONS ON POWER SYSTEMS (2019)

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Article Multidisciplinary Sciences

Visualizing a field of research: A methodology of systematic scientometric reviews

Chaomei Chen et al.

PLOS ONE (2019)

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Localization and Clustering Based on Swarm Intelligence in UAV Networks for Emergency Communications

Muhammad Yeasir Arafat et al.

IEEE INTERNET OF THINGS JOURNAL (2019)

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Review Energy & Fuels

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

Lefeng Cheng et al.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2019)

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Article Energy & Fuels

A Classification Method for Transmission Line Icing Process Curve Based on Hierarchical K-Means Clustering

Yanpeng Hao et al.

ENERGIES (2019)

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Article Computer Science, Information Systems

A Data Clustering Based Probabilistic Power Flow Method for AC/VSC-MTDC

Sajad Madadi et al.

IEEE SYSTEMS JOURNAL (2019)

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Article Computer Science, Information Systems

Markov Model-Based Energy Storage System Planning in Power Systems

Ying-Yi Hong et al.

IEEE SYSTEMS JOURNAL (2019)

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Prognostics of IGBT modules based on the approach of particle filtering

Yizhou Lu et al.

MICROELECTRONICS RELIABILITY (2019)

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SMEER: Secure Multi-tier Energy Efficient Routing Protocol for Hierarchical Wireless Sensor Networks

Geetika Dhand et al.

WIRELESS PERSONAL COMMUNICATIONS (2019)

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Article Telecommunications

Efficient artificial fish swarm based clustering approach on mobility aware energy-efficient for MANET

Deepak Gupta et al.

TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES (2019)

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Article Green & Sustainable Science & Technology

Optimization of multi-layer absorbing systems in solar flat-plate collectors using cluster analysis

Ali Khatibi et al.

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS (2019)

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Proceedings Paper Computer Science, Interdisciplinary Applications

K-means Clustering Using R A Case Study of Market Segmentation

Phan Duy Hung et al.

PROCEEDINGS OF THE 2019 5TH INTERNATIONAL CONFERENCE ON E-BUSINESS AND APPLICATIONS (ICEBA 2019) (2019)

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Article Computer Science, Information Systems

Controlled Islanding for a Hybrid AC/DC Grid with VSC-HVDC Using Semi-Supervised Spectral Clustering

Yang Li et al.

IEEE ACCESS (2019)

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Article Computer Science, Information Systems

A Feature-Reduction Multi-View k-Means Clustering Algorithm

Miin-Shen Yang et al.

IEEE ACCESS (2019)

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Proceedings Paper Computer Science, Theory & Methods

Short-term Load Forecasting with LSTM based Ensemble Learning

Lingxiao Wang et al.

2019 INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA) (2019)

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Proceedings Paper Telecommunications

Application of Big Data in Smart Grids: Energy Analytics

Azamat Marlen et al.

2019 21ST INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY (ICACT): ICT FOR 4TH INDUSTRIAL REVOLUTION (2019)

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Proceedings Paper Telecommunications

Comparative Analysis of Electricity Consumption at Home through a Silhouette-score prospective

Hyun Wong Choi et al.

2019 21ST INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY (ICACT): ICT FOR 4TH INDUSTRIAL REVOLUTION (2019)

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Proceedings Paper Engineering, Multidisciplinary

Machine Learning based Clustering for Identifying Power Quality Events

Pravin Shinde et al.

2019 IEEE 5TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT) (2019)

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Proceedings Paper Computer Science, Interdisciplinary Applications

A Comparative Study of Smart Grid Security Based on Unsupervised Learning and Load Ranking

Shuva Paul et al.

2019 IEEE INTERNATIONAL CONFERENCE ON ELECTRO INFORMATION TECHNOLOGY (EIT) (2019)

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Article Computer Science, Information Systems

Net Electricity Clustering at Different Temporal Resolutions Using a SAX-Based Method for Integrated Distribution System Planning

Michael Emmanuel et al.

IEEE ACCESS (2019)

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Article Computer Science, Information Systems

A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network

Assia Maamar et al.

CMC-COMPUTERS MATERIALS & CONTINUA (2019)

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Article Computer Science, Information Systems

Energy Efficiency Optimization-Based Joint Resource Allocation and Clustering Algorithm for M2M Communication Systems

Rong Chai et al.

IEEE ACCESS (2019)

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Article Computer Science, Information Systems

Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation

Bowen Zhou et al.

IEEE ACCESS (2019)

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Article Computer Science, Information Systems

Probabilistic Power Flow Analysis of Bulk Power System for Practical Grid Planning Application

Sungyoon Song et al.

IEEE ACCESS (2019)

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Article Computer Science, Information Systems

A Weekly Load Data Mining Approach Based on Hidden Markov Model

Shixiang Lu et al.

IEEE ACCESS (2019)

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Article Computer Science, Information Systems

Data-Driven Location Selection for Battery Swapping Stations

Ming Zeng et al.

IEEE ACCESS (2019)

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Article Computer Science, Information Systems

Application of Bidirectional Recurrent Neural Network Combined With Deep Belief Network in Short-Term Load Forecasting

Xianlun Tang et al.

IEEE ACCESS (2019)

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Article Engineering, Electrical & Electronic

Space-Time Approach for Disturbance Detection and Classification

Hamid Gharavi et al.

IEEE TRANSACTIONS ON SMART GRID (2018)

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Article Engineering, Electrical & Electronic

Economic optimization for configuration and sizing of micro integrated energy systems

Haibo Yu et al.

JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY (2018)

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Article Energy & Fuels

Quantitative synergy assessment of regional wind-solar energy resources based on MERRA reanalysis data

Hengxu Zhang et al.

APPLIED ENERGY (2018)

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Article Energy & Fuels

Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method

Yimei Wang et al.

ENERGIES (2018)

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Article Energy & Fuels

Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering

Arman Oshnoei et al.

ENERGIES (2018)

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Article Thermodynamics

Evaluation of a data driven stochastic approach to optimize the participation of a wind and storage power plant in day-ahead and reserve markets

Jose L. Crespo-Vazquez et al.

ENERGY (2018)

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An optimal renewable energy management strategy with and without hydropower using a factor weighted multi-criteria decision making analysis and nation-wide big data - Case study in Iran

Pouya Ifaei et al.

ENERGY (2018)

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Implementation of novel hybrid approaches for power curve modeling of wind turbines

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Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns

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ENERGY CONVERSION AND MANAGEMENT (2018)

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Efficient Simulation of Temperature Evolution of Overhead Transmission Lines Based on Analytical Solution and NWP

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IEEE TRANSACTIONS ON POWER DELIVERY (2018)

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Dynamic equivalent modeling of two-staged photovoltaic power station clusters based on dynamic affinity propagation clustering algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2018)

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2018 IEEE 15TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC) (2018)

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