Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Tu, Chia-Sheng; Hong, Chih-Ming; Huang, Hsin-Yi; Chen, Chiung-Hsing

doi:10.3390/en13236319

“…Tu, C.-S. et al [13] introduced a model for short-term wind power forecasting that relies on historical marine weather and wind power data. To enhance computing efficiency, they divided the dataset into clusters using a data regression algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network

Kanagarathinam,

Aruna,

Ravivarman

et al. 2023

Sustainability

3

0

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Integrating wind energy forecasting into urban city energy management systems offers significant potential for optimizing energy usage, reducing the carbon footprint, and improving overall energy efficiency. This article focuses on developing a wind power forecasting model using cutting-edge technologies to enhance urban city energy management systems. To effectively manage wind energy availability, a strategy is proposed to curtail energy consumption during periods of low wind energy availability and boost consumption during periods of high wind energy availability. For this purpose, an LSTM-based model is employed to forecast short-term wind power, leveraging a publicly available dataset. The LSTM model is trained with 27,310 instances and 10 wind energy system attributes, which were selected using the Pearson correlation feature selection method to identify crucial features. The evaluation of the LSTM-based forecasting model yields an impressive R2 score of 0.9107. The model’s performance metrics attest to its high accuracy, explaining a substantial proportion of the variance in the test data. This study not only contributes to advancing wind power forecasting, but also holds promise for sustainable urban energy management, enabling cities to make informed decisions in optimizing energy consumption and promoting a greener, more resilient future.

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“…The main advantage of ensemble models is their diversity, which allows for providing a set of multiple forecasts of the same quantity based on different estimates of initial atmospheric conditions in the WPPF, and ensemble approaches such as the CEEMDAN-IBA-GPR model [23], multi-feature fusion/self-attention mechanism/graph convolutional network (MFF-SAM-GCN), weighted multivariate time series motifs (WMTSM), and conditional LP (CLP) have been combined with adaptive boundary quantiles (ABQs), wavelet neural network (WNN) trained by the five algorithms [24][25][26][27], data preprocessing (EMD and ICEEMDAN) with parameter optimization [28], and enhanced bee swarm optimization (EBSO) to perform parameter optimization for least squares support vector machine [29] toward probabilistic wind power forecasting, taking full advantage of the most recent information and leveraging the strengths of multiple forecasting models. More recently developed are machine learning methods, which are powerful training algorithms based on artificial intelligence (i.e., neural networks).…”

Section: Introductionmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai

¹

,

Hong

²

,

Tu

³

et al. 2023

Self Cite

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

show abstract

“…The main advantage of ensemble models is their diversity, which allows providing a set of multiple forecasts of the same quantity based on different estimates of initial atmospheric conditions in the WPPF, and ensemble approaches such as the CEEMDAN-IBA-GPR model, multi-feature fusion/self-attention mechanism/graph convolutional network (MFF-SAM-GCN), weighted multivariate time series motifs (WMTSM), and conditional LP (CLP) have been combined with adaptive boundary quantiles (ABQs), wavelet neural network (WNN) trained by the five algorithms [24][25][26][27], data preprocessing (EMD and ICEEMDAN) with parameter optimization [28], and enhanced bee swarm optimization (EBSO) to perform parameter optimization for least squares support vector machine [29] toward probabilistic wind power forecasting, taking full advantage of the most recent information and leveraging the strengths of multiple forecasting models. More recently developed are machine learning methods, which are powerful training algorithms based on artificial intelligence (i.e., neural networks).…”

Section: Introductionmentioning

confidence: 99%

“…Due to their high computation intelligence and accuracy, such methods have been widely used in the past few years to improve the accuracy and performance of traditional WPPF models. The machine learning-based wind speed predictions for k-NN and conditional KDE, Adaboost-PSO-ELM, and enhanced bee swarm optimization (EBSO), to perform parameter optimization for least squares support vector machine (LSSVM) [11,[26][27][28][30][31] models, were proposed to identify meaningful training data to reduce the volume of modeling data and improve the computing efficiency. They have good generalization ability and robustness and can provide more accurate wind power forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai¹,

Hong²,

Lin³

et al. 2023

Preprint

7

0

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

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Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Cited by 14 publications

References 23 publications

Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network

Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network

A Review of Modern Wind Power Generation Forecasting Technologies

A Review of Modern Wind Power Generation Forecasting Technologies

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