4.7 Article

Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis

Related references

Note: Only part of the references are listed.
Article Energy & Fuels

Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms

Pawel Piotrowski et al.

Summary: The ability to accurately forecast power generation in large wind farms is crucial for system security and economic savings. Using statistical analysis and innovative prediction methods, the study demonstrates the effectiveness of Numerical Weather Prediction (NWP) forecasts for hourly lags and the use of ensemble methods for improved accuracy. Machine learning solutions and original ensemble methods outperform traditional single methods in reducing errors in energy generation forecasts for wind farms, with the Ensemble Averaging Without Extremes method showing the lowest normalized mean absolute error. The proposed ensemble methods are applicable not only to wind energy but also to other renewable sources like hydropower and photovoltaic systems.

ENERGIES (2022)

Article Environmental Sciences

Short-term wind speed prediction using hybrid machine learning techniques

Deepak Gupta et al.

Summary: Wind energy is a potential renewable energy source globally. Accurate prediction of wind speed is crucial for estimating wind power accurately. Hybrid machine learning models were used in this study for short-term wind speed prediction, with LDMR model outperforming others in prediction accuracy and ELM model being computationally faster.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022)

Article Energy & Fuels

Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study

Abdulelah Alkesaiberi et al.

Summary: This study aims to develop efficient data-driven models to accurately forecast wind power generation. By investigating different machine learning models and incorporating dynamic information, the forecasting performance has been improved. The results demonstrate that considering lagged data and more input variables can lead to better wind power prediction, and the optimized GPR and ensemble models outperform other machine learning models.

ENERGIES (2022)

Article Thermodynamics

Machine learning methods to assist structure design and optimization of Dual Darrieus Wind Turbines

Biyi Cheng et al.

Summary: This study develops a bi-level structure design and optimization model based on machine learning algorithms to optimize the optimal structure of wind turbines. By combining prediction models and optimization models, it achieves a balance between solution accuracy and computational cost. The training dataset, generated by orthogonal testing and computational fluid dynamics simulation, produces results with the same accuracy as traditional models.

ENERGY (2022)

Article Engineering, Electrical & Electronic

A novel hybrid approach based on variational heteroscedastic Gaussian process regression for multi-step ahead wind speed forecasting

Chu Zhang et al.

Summary: Accurate wind speed forecasting is crucial for the safe and economical operation of electric power and energy systems. This study introduces the variational hetemscedastic GPR model with Marginalized Variational approximation and CEEMDAN decomposition strategy for enhanced forecasting performance on wind speed time series, showing better results compared to twelve benchmark models.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

A novel hybrid model for short-term prediction of wind speed

Haize Hu et al.

Summary: In this paper, a new hybrid model based on gray wolf algorithm (GWO) and support vector machine (SVM) is proposed for accurate wind speed prediction. The model combines Neo4j, k-means clustering, GWO, and SVM to preprocess and analyze data, optimize parameters, and accurately predict wind speed. Experimental results demonstrate the model's high accuracy, stability, and acceptable time complexity, which can provide a scientific basis for improving the operation security and stability of power systems.

PATTERN RECOGNITION (2022)

Article Green & Sustainable Science & Technology

Short term wind energy prediction model based on data decomposition and optimized LSSVM

Yagang Zhang et al.

Summary: This paper addresses the uncertainty and instability of wind power and proposes a combined model to accurately predict wind speed by using feature extraction and prediction algorithms, reducing wind power forecast errors and improving forecast accuracy.

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS (2022)

Article Thermodynamics

An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction

Chu Zhang et al.

Summary: In this study, a hybrid deep learning model based on convolutional neural network (CNN), Bidirectional long short-term memory (BiLSTM), improved sine cosine algorithm (ISCA) and time-varying filter based empirical mode decomposition (TVFEMD) is proposed for accurate wind speed prediction. The model decomposes the data, analyzes the importance of subcomponents, and utilizes CNN-BiLSTM and ISCA for prediction. Experimental results show that the proposed model achieves good prediction results on all data sets.

ENERGY (2022)

Article Energy & Fuels

Hourly day ahead wind speed forecasting based on a hybrid model of EEMD, CNN-Bi-LSTM embedded with GA optimization

Thi Hoai Thu Nguyen et al.

Summary: In this paper, a novel hybrid model combining decomposition and deep learning, embedded with GA optimization, was proposed for wind speed forecasting. By decomposing and training the historical wind speed time series, better forecasting results than other methods were obtained.

ENERGY REPORTS (2022)

Article Energy & Fuels

One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques

Konstantinos Blazakis et al.

Summary: The integration of renewable energy sources into power systems is a major concern due to the increasing demand for electric energy. However, the fluctuation in solar irradiation and windspeed makes it difficult to incorporate solar and wind power generation into electricity networks. Therefore, accurate forecasting of solar irradiation and windspeed is crucial for the safe and reliable operation of electrical systems. This study adopts deep learning techniques and compares them with conventional methods for medium-term forecasting, taking into consideration the influence of clouds on solar irradiation and the seasonal similarity of windspeed patterns. The results demonstrate high forecasting performance.

ENERGIES (2022)

Article Information Science & Library Science

Short term wind power forecasting using k-nearest neighbour (KNN)

Rahul Mahaseth et al.

Summary: This project focuses on predicting energy power based on previous 2 years' data using machine learning algorithms. By analyzing data on a yearly basis, the wind power generation is predicted for different locations taking into account geographical, demographic, wind speed, and weather conditions. The study found that the KNN algorithm is the most effective for wind power prediction.

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES (2022)

Article Engineering, Civil

Explainable extreme gradient boosting tree-based prediction of load-carrying capacity of FRP-RC columns

Abdoulaye Sanni Bakouregui et al.

Summary: This study introduces a new method for predicting the load-carrying capacity of FRP-RC columns using the XGBoost algorithm, outperforming other numerical equations. Important input variables for predicting the maximum axial load-carrying capacity include eccentricity ratio, gross sectional area, compressive strength of concrete, etc.

ENGINEERING STRUCTURES (2021)

Article Environmental Sciences

USA carbon neutrality target: Evaluating the role of environmentally adjusted multifactor productivity growth in limiting carbon emissions

Hongru Yang et al.

Summary: The study investigates the significant role of green growth in limiting carbon emissions in the USA, with results showing that green growth, output, renewable energy, and globalization are important factors affecting CO2 emissions. The results of frequency causality test indicate a unidirectional causal relationship from output, renewable energy consumption, green growth, and globalization to CO2 emissions.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2021)

Article Engineering, Civil

Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements

De-Cheng Feng et al.

Summary: This paper introduces a practical and comprehensive implementation of ensemble methods for predicting the shear strength of deep beams with or without web reinforcements. The study utilizes four typical ensemble machine learning models and demonstrates their superior performance over traditional machine learning methods in predicting accuracy and discrepancy.

ENGINEERING STRUCTURES (2021)

Article Engineering, Civil

Boosting machines for predicting shear strength of CFS channels with staggered web perforations

V. V. Degtyarev et al.

Summary: This study explores the use of five machine learning boosting algorithms to predict the shear strength properties of CFS channels with staggered web perforations. The models developed show high accuracy compared to existing equations, with CatBoost being the most accurate. The models were validated using a large dataset and show the ability to capture the underlying physics of the system.

STRUCTURES (2021)

Article Energy & Fuels

Quantile based probabilistic wind turbine power curve model*

Keyi Xu et al.

Summary: This paper introduces a novel concept called quantile power curve, which can generate a series of power curves at any confidence level, and proposes a neural network algorithm based on quantile loss to establish this curve. Through validation with operational data from a wind farm in China, it is demonstrated that the quantile power curve provides more comprehensive information about uncertainty during power generation and helps improve the renewable energy supply rate.

APPLIED ENERGY (2021)

Article Computer Science, Hardware & Architecture

Multi-agent reinforcement learning for cost-aware collaborative task execution in energy-harvesting D2D networks

Binbin Huang et al.

Summary: This paper discusses a cost-aware collaborative task-execution scheme in energy harvesting D2D networks, and the experimental results demonstrate that the scheme can effectively improve system efficiency and increase the number of completed tasks, while reducing task latency and dropout rate.

COMPUTER NETWORKS (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)

Article Green & Sustainable Science & Technology

Artificial Neural Networks based wake model for power prediction of wind farm

Zilong Ti et al.

Summary: A novel machine-learning-based wake model is developed in this study to improve the power prediction accuracy of wind farms, with high computational efficiency. The model establishes the implicit relationship between inflows and wake flows using Artificial Neural Networks based on massive CFD simulation dataset. The validated model shows significant improvements in power predictions compared to existing analytical models, matching well with LES and measurement data.

RENEWABLE ENERGY (2021)

Article Business

The market challenge of wind turbine industry-renewable energy in PR China and Germany

Victor Chang et al.

Summary: This paper explores the role of global Industry 4.0 technology management in the growth of the wind turbine industry, emphasizing the significance of legal policies, patents, and company analysis in shaping the development of the industry.

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE (2021)

Article Energy & Fuels

Sustainable Solutions for Green Financing and Investment in Renewable Energy Projects

Farhad Taghizadeh-Hesary et al.

ENERGIES (2020)

Article Green & Sustainable Science & Technology

Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm

Ling-Ling Li et al.

JOURNAL OF CLEANER PRODUCTION (2020)

Article Meteorology & Atmospheric Sciences

Long-term modelling of wind speeds using six different heuristic artificial intelligence approaches

Saman Maroufpoor et al.

INTERNATIONAL JOURNAL OF CLIMATOLOGY (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 Thermodynamics

Advanced wind power prediction based on data-driven error correction

Jing Yan et al.

ENERGY CONVERSION AND MANAGEMENT (2019)

Proceedings Paper Engineering, Multidisciplinary

Wind Power Prediction Based On Improved Genetic Algorithm and Support Vector Machine

Li Zhang et al.

2018 4TH INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND MATERIAL APPLICATION (2019)

Review Economics

Machine learning in energy economics and finance: A review

Hamed Ghoddusi et al.

ENERGY ECONOMICS (2019)

Article Construction & Building Technology

Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm

Ali Behnood et al.

CONSTRUCTION AND BUILDING MATERIALS (2017)

Review Engineering, Multidisciplinary

A review of renewable energy sources, sustainability issues and climate change mitigation

Phebe Asantewaa Owusu et al.

COGENT ENGINEERING (2016)

Review Economics

Depletion of fossil fuels and anthropogenic climate change-A review

Mikael Hook et al.

ENERGY POLICY (2013)

Review Green & Sustainable Science & Technology

Assessment of wind power potential for turbine installation in coastal areas of Turkey

Aynur Ucar et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2010)

Article Green & Sustainable Science & Technology

Wind tunnel and numerical study of a small vertical axis wind turbine

Robert Howell et al.

RENEWABLE ENERGY (2010)