4.1 Review

Advances of machine learning in multi-energy district communities? mechanisms, applications and perspectives

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Green & Sustainable Science & Technology

Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm

Tanveer Ahmad et al.

Summary: The current trend indicates that energy demand and supply will eventually be controlled by autonomous software that optimizes decision-making and energy distribution operations. New state-of-the-art machine learning (ML) technologies are integral in optimizing decision-making in energy distribution networks and systems. This study focuses on the urgent need to research data-driven probabilistic ML techniques that can be applied in smart energy systems and networks. The study examines the use of ML in core energy technologies and energy distribution utilities, highlighting their potential in areas such as energy material manufacturing, renewable energy integration, and big data analytics.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2022)

Article Green & Sustainable Science & Technology

Transition towards carbon-neutral districts based on storage techniques and spatiotemporal energy sharing with electrification and hydrogenation

Yuekuan Zhou

Summary: This study provides a systematic and comprehensive review on the transition towards carbon-neutral districts, focusing on energy storage techniques, spatiotemporal energy sharing, electrification, and hydrogenation. The research results can serve as important references for optimal planning on national energy strategies, technical guidelines, and economic incentives.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2022)

Review Green & Sustainable Science & Technology

Machine learning in photovoltaic systems: A review

Jorge Felipe Gaviria et al.

Summary: This paper provides a comprehensive review of the latest machine learning techniques applied to photovoltaic systems, focusing particularly on deep learning. It evaluates the use of ML in various aspects of PV systems, including control, islanding detection, management, fault detection and diagnosis, forecasting irradiance and power generation, sizing, and site adaptation. The paper presents over 100 research articles from the past five years that employ state-of-the-art ML techniques in PV systems, and provides open data sets, source code, and simulation environments for testing ML algorithms. It also includes case studies with open-source code and data for researchers interested in implementing up-to-date ML techniques in PV systems, and offers insights and possibilities for future development.

RENEWABLE ENERGY (2022)

Article Energy & Fuels

Multi objective demand side storage dispatch using hybrid extreme learning machine trained neural networks in a smart grid

Shobhit Nandkeolyar et al.

Summary: The typical mode of operation of electric energy systems is unidirectional and follows a top-down approach. However, researchers and industries are increasingly focused on the demand side in order to manage the balance between supply and demand. Demand Side Management (DSM) plays an important role in maintaining the reliable operation of the grid by managing and matching customer load with the supply system.

JOURNAL OF ENERGY STORAGE (2022)

Review Computer Science, Artificial Intelligence

A review on 5G technology for smart energy management and smart buildings in Singapore

Ghasan Fahim Huseien et al.

Summary: Sustainable and smart building is gaining momentum globally, and 5G technology plays a significant role in the construction, operation, and management of buildings. This study discusses the international trends in 5G applications for smart buildings, the research and development conducted in 5G labs, and the supported projects by the Singapore government. It serves as a benchmark for the future progress of smart cities in the context of big data.

ENERGY AND AI (2022)

Article Thermodynamics

A regression learner-based approach for battery cycling ageing predictiondadvances in energy management strategy and techno- economic analysis

Yuekuan Zhou

Summary: A nonlinear mathematical model is developed to explore effective strategies for smart grids. A regression learner-based battery cycling ageing prediction method is proposed, along with a machine learning algorithm selection approach. A novel battery discharging control strategy is also proposed. The study provides guidance for renewable energy planning, electrochemical battery storages, and advanced energy management strategies.

ENERGY (2022)

Article Energy & Fuels

Quantification on fuel cell degradation and techno-economic analysis of a hydrogen-based grid-interactive residential energy sharing network with fuel-cell-powered vehicles

Yingdong He et al.

Summary: The study presents a dynamic analysis on fuel cell degradation in a hydrogen-based building-vehicle energy network, revealing a total fuel cell degradation of 3.16% per vehicle within one year, with 2.5% from daily transportation and 0.66% from V2G interactions. In the H-2-based residential community, the total fuel cell degradation cost is US$6945.2, accounting for 33.4% of the total operating cost at $20770.61.

APPLIED ENERGY (2021)

Article Energy & Fuels

Comparing deep learning models for multi energy vectors prediction on multiple types of building

Lei Gao et al.

Summary: This study utilized deep learning models for multi energy vectors prediction on different building types, showing that the long short-term memory model performed the best in terms of both absolute and relative errors. For the majority of tasks, the CVRMSE was lower than 20%.

APPLIED ENERGY (2021)

Article Energy & Fuels

An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes

Helder R. O. Rocha et al.

Summary: A new methodology combining three different Artificial Intelligence techniques is proposed to solve energy demand planning in Smart Homes. By applying demand-side management to a multi-objective scheduling problem, a compromise between energy cost and user comfort is achieved.

APPLIED ENERGY (2021)

Review Green & Sustainable Science & Technology

Data-driven predictive control for unlocking building energy flexibility: A review

Anjukan Kathirgamanathan et al.

Summary: Managing supply and demand in the electricity grid becomes more challenging with the increasing penetration of variable renewable energy sources. Buildings are expected to have an expanding role in the future smart grid through better grid integration and predictive control. Data-driven predictive control, coupled with the Internet of Things, holds promise for scalable and transferrable approaches in grid integration of buildings.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)

Article Construction & Building Technology

Machine-learning-based multi-step heat demand forecasting in a district heating system

Primoz Potocnik et al.

Summary: This study developed a machine learning based short-term heat demand forecasting approach for the largest Slovenian district heating system, achieving good prediction results. The Gaussian process regression (GPR) model performed the best among all models, emphasizing the importance of accurate temperature forecasts.

ENERGY AND BUILDINGS (2021)

Article Thermodynamics

Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources

Linfei Yin et al.

Summary: The article introduces a three-state energy model and a unified time-scale coordinated primary voltage control framework for flexible energy sources connected to smart grids, demonstrating superior performance compared to traditional PID and DNN control methods.

ENERGY (2021)

Article Construction & Building Technology

Machine learning-based cooling load prediction and optimal control for mechanical ventilative cooling in high-rise buildings

Haohan Sha et al.

Summary: This study developed an optimal control method for mechanical ventilative cooling in high-rise buildings and evaluated its energy performance under the impact of climate change. Results show that energy savings from mechanical ventilative cooling will vary in different seasons due to climate change.

ENERGY AND BUILDINGS (2021)

Article Green & Sustainable Science & Technology

A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids

Sheraz Aslam et al.

Summary: Microgrids combining renewable energy sources, energy storage devices, and load management methods face challenges due to the intermittent nature of renewables. Forecasting power generation from renewables is crucial for efficient grid operations and optimal resource utilization. Machine learning and deep learning models show promise in predicting energy demand and generation, with the efficiency of forecasting methods depending on historical data availability.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)

Review Chemistry, Physical

The role of artificial intelligence in the mass adoption of electric vehicles

Moin Ahmed et al.

Summary: The sales of electric vehicles have been increasing steadily, mainly due to improvements in cost and performance, increased options available to consumers, and growing environmental awareness. However, challenges such as limited driving range, long charging times, and insufficient infrastructure still hinder the rapid and widespread adoption of EVs.
Article Computer Science, Artificial Intelligence

Data-driven approaches for cyber defense of battery energy storage systems

Nina Kharlamova et al.

Summary: Battery energy storage systems (BESS) are crucial in modern power systems for integrating renewable energy sources, but are vulnerable to cyber threats. A comprehensive review on design requirements and attack detection methods is still lacking, emphasizing the need for a cyber defense strategy to ensure secure operation of BESS.

ENERGY AND AI (2021)

Article Computer Science, Artificial Intelligence

Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates

Si Chen et al.

Summary: This study proposes two approaches for fitting and predicting electricity demand of office buildings, one splitting data into working and non-working hours, the other using ANN and fuzzy logic to fit multiple variables. Both approaches show improved accuracy in predicting building power demand, providing valuable information for building energy management without additional parameters.

ENERGY AND AI (2021)

Review Automation & Control Systems

A review of machine learning for new generation smart dispatch in power systems

Linfei Yin et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2020)

Article Chemistry, Multidisciplinary

A Universal Machine Learning Algorithm for Large-Scale Screening of Materials

George S. Fanourgakis et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2020)

Article Green & Sustainable Science & Technology

Machine learning models to quantify and map daily global solar radiation and photovoltaic power

Yu Feng et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)

Article Green & Sustainable Science & Technology

A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea

KiJeon Nam et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)

Article Energy & Fuels

Operation scheduling in a solar thermal system: A reinforcement learning-based framework

Camila Correa-Jullian et al.

APPLIED ENERGY (2020)

Article Energy & Fuels

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

Muhammad Sohail Ibrahim et al.

APPLIED ENERGY (2020)

Article Construction & Building Technology

Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings

Silvio Brandi et al.

ENERGY AND BUILDINGS (2020)

Article Construction & Building Technology

Unsupervised learning for feature projection: Extracting patterns from multidimensional building measurements

Chunze Xiao et al.

ENERGY AND BUILDINGS (2020)

Review Green & Sustainable Science & Technology

Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review

Ioannis Antonopoulos et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)

Article Engineering, Electrical & Electronic

Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning

Junyan Hu et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

Review Green & Sustainable Science & Technology

Machine learning applications in urban building energy performance forecasting: A systematic review

Soheil Fathi et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)

Article Computer Science, Artificial Intelligence

Reinforcement learning for whole-building HVAC control and demand response

Donald Azuatalam et al.

ENERGY AND AI (2020)

Article Engineering, Electrical & Electronic

Actor-critic learning for optimal building energy management with phase change materials

Zahra Rahimpour et al.

ELECTRIC POWER SYSTEMS RESEARCH (2020)

Article Automation & Control Systems

Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries

Robert R. Richardson et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

Article Engineering, Electrical & Electronic

On-Line Building Energy Optimization Using Deep Reinforcement Learning

Elena Mocanu et al.

IEEE TRANSACTIONS ON SMART GRID (2019)

Review Energy & Fuels

Reinforcement learning for demand response: A review of algorithms and modeling techniques

Jose R. Vazquez-Canteli et al.

APPLIED ENERGY (2019)

Article Thermodynamics

Optimization of critical parameters of PEM fuel cell using TLBO-DE based on Elman neural network

Chengjun Guo et al.

ENERGY CONVERSION AND MANAGEMENT (2019)

Article Green & Sustainable Science & Technology

Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

Alfredo Arcos Jimenez et al.

RENEWABLE ENERGY (2019)

Article Energy & Fuels

Data-driven prediction of battery cycle life before capacity degradation

Kristen A. Severson et al.

NATURE ENERGY (2019)

Review Green & Sustainable Science & Technology

Machine learning methods for wind turbine condition monitoring: A review

Adrian Stetco et al.

RENEWABLE ENERGY (2019)

Article Energy & Fuels

Machine learning methods to assist energy system optimization

A. T. D. Perera et al.

APPLIED ENERGY (2019)

Article Engineering, Chemical

Machine learning models for solvent effects on electric double layer capacitance

Haiping Su et al.

CHEMICAL ENGINEERING SCIENCE (2019)

Article Green & Sustainable Science & Technology

Analysis and diagnosis of PEM fuel cell failure modes (flooding & drying) across the physical parameters of electrochemical impedance model: Using neural networks method

Slimane Laribi et al.

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS (2019)

Article Thermodynamics

Wind power forecasting based on daily wind speed data using machine learning algorithms

Halil Demolli et al.

ENERGY CONVERSION AND MANAGEMENT (2019)

Article Chemistry, Physical

Remaining useful life prediction for supercapacitor based on long short-term memory neural network

Yanting Zhou et al.

JOURNAL OF POWER SOURCES (2019)

Article Construction & Building Technology

Multivariable optimisation of a new PCMs integrated hybrid renewable system with active cooling and hybrid ventilations

Yuekuan Zhou et al.

JOURNAL OF BUILDING ENGINEERING (2019)

Article Construction & Building Technology

Early predicting cooling loads for energy-efficient design in office buildings by machine learning

Ngoc-Tri Ngo

ENERGY AND BUILDINGS (2019)

Article Green & Sustainable Science & Technology

Modified PSO algorithm for real-time energy management in grid-connected microgrids

Md Alamgir Hossain et al.

RENEWABLE ENERGY (2019)

Editorial Material Chemistry, Physical

Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization

Dilip Krishnamurthy et al.

ACS ENERGY LETTERS (2019)

Article Construction & Building Technology

Forecasting performance comparison of two hybrid machine learning models for cooling load of a large-scale commercial building

Zhou Xuan et al.

JOURNAL OF BUILDING ENGINEERING (2019)

Article Chemistry, Physical

Design of multifunctional supercapacitor electrodes using an informatics approach

Anish G. Patel et al.

MOLECULAR SYSTEMS DESIGN & ENGINEERING (2019)

Review Chemistry, Physical

Machine learning for renewable energy materials

Geun Ho Gu et al.

JOURNAL OF MATERIALS CHEMISTRY A (2019)

Article Construction & Building Technology

Model input selection for building heating load prediction: A case study for an office building in Tianjin

Yan Ding et al.

ENERGY AND BUILDINGS (2018)

Article Thermodynamics

Short-term wind speed prediction using an extreme learning machine model with error correction

Lili Wang et al.

ENERGY CONVERSION AND MANAGEMENT (2018)

Article Engineering, Electrical & Electronic

Transforming Energy Networks via Peer-to-Peer Energy Trading The potential of game-theoretic approaches

Wayes Tushar et al.

IEEE SIGNAL PROCESSING MAGAZINE (2018)

Article Automation & Control Systems

Battery State-of-Charge Estimation Based on Regular/Recurrent Gaussian Process Regression

Gozde O. Sahinoglu et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

Review Green & Sustainable Science & Technology

Feature selection in machine learning prediction systems for renewable energy applications

S. Salcedo-Sanz et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)

Review Green & Sustainable Science & Technology

A review of supercapacitor modeling, estimation, and applications: A control/management perspective

Lei Zhang et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)

Review Green & Sustainable Science & Technology

Estimating heating load in buildings using multivariate adaptive regression splines, extreme learning machine, a hybrid model of MARS and ELM

Sanjiban Sekhar Roy et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)

Review Green & Sustainable Science & Technology

Fault detection and diagnosis methods for photovoltaic systems: A review

A. Mellit et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)

Article Green & Sustainable Science & Technology

Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees

Muhammad Waseem Ahmad et al.

JOURNAL OF CLEANER PRODUCTION (2018)

Article Chemistry, Physical

State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

Ephrem Chemali et al.

JOURNAL OF POWER SOURCES (2018)

Article Materials Science, Multidisciplinary

Artificial neural network enabled capacitance prediction for carbon-based supercapacitors

Shan Zhu et al.

MATERIALS LETTERS (2018)

Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

Review Green & Sustainable Science & Technology

Deep Learning for fault detection in wind turbines

Georg Helbing et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)

Article Thermodynamics

An enhanced machine learning based approach for failures detection and diagnosis of PV systems

Elyes Garoudja et al.

ENERGY CONVERSION AND MANAGEMENT (2017)

Review Engineering, Electrical & Electronic

Adaptive intelligent techniques for microgrid control systems: A survey

Magdi S. Mahmoud et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2017)

Article Chemistry, Physical

Gaussian process regression for forecasting battery state of health

Robert R. Richardson et al.

JOURNAL OF POWER SOURCES (2017)

Review Green & Sustainable Science & Technology

A review and analysis of regression and machine learning models on commercial building electricity load forecasting

B. Yildiz et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2017)

Article Engineering, Electrical & Electronic

Multiagent-Based Optimal Microgrid Control Using Fully Distributed Diffusion Strategy

Ricardo de Azevedo et al.

IEEE TRANSACTIONS ON SMART GRID (2017)

Article Green & Sustainable Science & Technology

Optimal management of renewable energy sources by virtual power plant

Mohammad Javad Kasaei et al.

RENEWABLE ENERGY (2017)

Article Construction & Building Technology

PV array fault DiagnosticTechnique for BIPV systems

Oussama Hachana et al.

ENERGY AND BUILDINGS (2016)

Article Construction & Building Technology

Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks

Chirag Deb et al.

ENERGY AND BUILDINGS (2016)

Article Construction & Building Technology

Applied machine learning: Forecasting heat load in district heating system

Samuel Idowu et al.

ENERGY AND BUILDINGS (2016)

Article Engineering, Electrical & Electronic

Fault Diagnosis of Photovoltaic Panels Using Dynamic Current-Voltage Characteristics

Wenguan Wang et al.

IEEE TRANSACTIONS ON POWER ELECTRONICS (2016)

Article Engineering, Electrical & Electronic

An improved particle swarm optimisation algorithm applied to battery sizing for stand-alone hybrid power systems

Ce Shang et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2016)

Article Chemistry, Physical

Modeling of commercial proton exchange membrane fuel cell using support vector machine

Azadeh Kheirandish et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2016)

Article Chemistry, Physical

Robust adaptive neural network control for PEM fuel cell

Alireza Abbaspour et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2016)

Article Chemistry, Physical

A novel health indicator for on-line lithium-ion batteries remaining useful life prediction

Yapeng Zhou et al.

JOURNAL OF POWER SOURCES (2016)

Review Green & Sustainable Science & Technology

Big data driven smart energy management: From big data to big insights

Kaile Zhou et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2016)

Review Green & Sustainable Science & Technology

Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models

Jianzhou Wang et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2016)

Review Green & Sustainable Science & Technology

Microgrid supervisory controllers and energy management systems: A literature review

Lexuan Meng et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2016)

Article Computer Science, Interdisciplinary Applications

Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules

H. Mekki et al.

SIMULATION MODELLING PRACTICE AND THEORY (2016)

Article Automation & Control Systems

Predictive pitch control of an electro-hydraulic digital pitch system for wind turbines based on the extreme learning machine

Xiu-xing Yin et al.

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL (2016)

Article Engineering, Electrical & Electronic

Graph-Based Semi-supervised Learning for Fault Detection and Classification in Solar Photovoltaic Arrays

Ye Zhao et al.

IEEE TRANSACTIONS ON POWER ELECTRONICS (2015)

Article Engineering, Electrical & Electronic

Development of nano fiber MnO2 thin film electrode and cyclic voltammetry behavior modeling using artificial neural network for supercapacitor application

T. D. Dongale et al.

MATERIALS SCIENCE IN SEMICONDUCTOR PROCESSING (2015)

Article Energy & Fuels

Diagnostic method for photovoltaic systems based on light I-V measurements

Sergiu Spataru et al.

SOLAR ENERGY (2015)

Article Engineering, Electrical & Electronic

Optimal Demand Response Using Device-Based Reinforcement Learning

Zheng Wen et al.

IEEE TRANSACTIONS ON SMART GRID (2015)

Article Thermodynamics

Performance prediction of a solar thermal energy system using artificial neural networks

Wahiba Yaici et al.

APPLIED THERMAL ENGINEERING (2014)

Article Engineering, Electrical & Electronic

Modeling and adaptive control for supercapacitor in automotive applications based on artificial neural networks

Akram Eddahech et al.

ELECTRIC POWER SYSTEMS RESEARCH (2014)

Article Green & Sustainable Science & Technology

Multiagent-Based Hybrid Energy Management System for Microgrids

Meiqin Mao et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2014)

Article Thermodynamics

Automatic fault diagnosis in PV systems with distributed MPPT

J. Solorzano et al.

ENERGY CONVERSION AND MANAGEMENT (2013)

Article Thermodynamics

An efficient fault diagnosis method for PV systems based on operating voltage-window

Nuri Gokmen et al.

ENERGY CONVERSION AND MANAGEMENT (2013)

Article Engineering, Electrical & Electronic

Comprehensive Real-Time Microgrid Power Management and Control With Distributed Agents

C. M. Colson et al.

IEEE TRANSACTIONS ON SMART GRID (2013)

Article Engineering, Electrical & Electronic

Multi-Agent Based Hierarchical Hybrid Control for Smart Microgrid

Chun-Xia Dou et al.

IEEE TRANSACTIONS ON SMART GRID (2013)

Article Engineering, Electrical & Electronic

Deep Learning: Methods and Applications

Li Deng et al.

FOUNDATIONS AND TRENDS IN SIGNAL PROCESSING (2013)

Article Energy & Fuels

Monitoring of wind farms' power curves using machine learning techniques

Antonino Marvuglia et al.

APPLIED ENERGY (2012)

Article Construction & Building Technology

Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools

Athanasios Tsanas et al.

ENERGY AND BUILDINGS (2012)

Article Thermodynamics

On-line supercapacitor dynamic models for energy conversion and management

C. H. Wu et al.

ENERGY CONVERSION AND MANAGEMENT (2012)

Article Automation & Control Systems

Extreme Learning Machine for Regression and Multiclass Classification

Guang-Bin Huang et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS (2012)

Article Engineering, Electrical & Electronic

Multiagent System for Real-Time Operation of a Microgrid in Real-Time Digital Simulator

Thillainathan Logenthiran et al.

IEEE TRANSACTIONS ON SMART GRID (2012)

Review Green & Sustainable Science & Technology

Power management strategy in the alternative energy photovoltaic/PEM Fuel Cell hybrid system

M. Hatti et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2011)

Article Computer Science, Interdisciplinary Applications

Energy performance and degradation over 20 years performance of BP c-Si PV modules

S. Kaplanis et al.

SIMULATION MODELLING PRACTICE AND THEORY (2011)

Article Thermodynamics

Automatic supervision and fault detection of PV systems based on power losses analysis

A. Chouder et al.

ENERGY CONVERSION AND MANAGEMENT (2010)

Article Energy & Fuels

Neural network modelling of thermal stratification in a solar DHW storage

P. Geczy-Vig et al.

SOLAR ENERGY (2010)

Article Computer Science, Artificial Intelligence

Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks

Ahmet Serdar Yilmaz et al.

EXPERT SYSTEMS WITH APPLICATIONS (2009)

Article Automation & Control Systems

Descriptive and Inferential Statistics for Supervising and Monitoring the Operation of PV Plants

Silvano Vergura et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2009)

Review Green & Sustainable Science & Technology

Condition monitoring and fault detection of wind turbines and related algorithms: A review

Z. Hameed et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2009)

Article Thermodynamics

Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network

Kemal Ermis et al.

INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER (2007)

Article Green & Sustainable Science & Technology

Predictive control of an integrated PV-diesel water and power supply system using an artificial neural network

Ali Al-Alawi et al.

RENEWABLE ENERGY (2007)

Article Materials Science, Multidisciplinary

Artificial neural network simulator for supercapacitor performance prediction

Hossein Farsi et al.

COMPUTATIONAL MATERIALS SCIENCE (2007)

Article Engineering, Electrical & Electronic

Application of neural network controller for maximum power extraction of a grid-connected wind turbine system

K Ro et al.

ELECTRICAL ENGINEERING (2005)

Article Green & Sustainable Science & Technology

Maximum power point traking controller for PV systems using neural networks

ABG Bahgat et al.

RENEWABLE ENERGY (2005)

Article Energy & Fuels

Ann based peak power tracking for PV supplied DC motors

M Veerachary et al.

SOLAR ENERGY (2000)