Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning

The goal of the research is to increase the accuracy of wind power forecasts while maintaining the power system’s stability and safety. First, the wireless sensor network (WSN) is used to collect the meteorological data of wind power plants in real time. Second, the real-time data collected by WSN a...

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Vydáno v:	Mathematical problems in engineering Ročník 2022; s. 1 - 10
Hlavní autoři:	Tian, Weihua, Bao, Yan, Liu, Wei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Hindawi 28.04.2022 John Wiley & Sons, Inc
Témata:	Accuracy Algorithms Artificial intelligence Artificial neural networks Back propagation networks Deep learning Electromagnetic radiation Energy Machine learning Mathematical models Meteorological data Methods Neural networks Optimization Power plants Real time Receiving Root-mean-square errors Sensors Signal reception Signal strength Turbines Weather forecasting Wind power Wireless communications Wireless networks Wireless sensor networks
ISSN:	1024-123X, 1563-5147
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Abstract	The goal of the research is to increase the accuracy of wind power forecasts while maintaining the power system’s stability and safety. First, the wireless sensor network (WSN) is used to collect the meteorological data of wind power plants in real time. Second, the real-time data collected by WSN are combined with the meteorological forecast data of some meteorological organizations. Then, the fruit fly optimization algorithm (FOA) is improved, and the improved fruit fly optimization algorithm (IFOA) and back propagation neural network (BPNN) are combined to construct the wind power forecast model. Finally, the signal reception of the WSN and the error of wind power forecast under different receiving distances and different antenna heights are tested. The results show that with the increase of receiving and transmitting distance, the signal strength decreases, the packet loss rate increases, and the electromagnetic wave of wind plants will cause some interference to the signal strength. The fly optimization algorithm-back propagation (IFOA-BP) wind power forecast model has a better effect than other models in wind power prediction and can better fit the actual tested wind power. Its root mean square error (RMSE) and mean absolute error (MAE) are 0.16 and 0.11, respectively. The research results provide a reference for improving the forecast accuracy of wind power.
AbstractList	The goal of the research is to increase the accuracy of wind power forecasts while maintaining the power system’s stability and safety. First, the wireless sensor network (WSN) is used to collect the meteorological data of wind power plants in real time. Second, the real-time data collected by WSN are combined with the meteorological forecast data of some meteorological organizations. Then, the fruit fly optimization algorithm (FOA) is improved, and the improved fruit fly optimization algorithm (IFOA) and back propagation neural network (BPNN) are combined to construct the wind power forecast model. Finally, the signal reception of the WSN and the error of wind power forecast under different receiving distances and different antenna heights are tested. The results show that with the increase of receiving and transmitting distance, the signal strength decreases, the packet loss rate increases, and the electromagnetic wave of wind plants will cause some interference to the signal strength. The fly optimization algorithm-back propagation (IFOA-BP) wind power forecast model has a better effect than other models in wind power prediction and can better fit the actual tested wind power. Its root mean square error (RMSE) and mean absolute error (MAE) are 0.16 and 0.11, respectively. The research results provide a reference for improving the forecast accuracy of wind power.
Author	Liu, Wei Bao, Yan Tian, Weihua
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Copyright	Copyright © 2022 Weihua Tian et al. Copyright © 2022 Weihua Tian et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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SubjectTerms	Accuracy Algorithms Artificial intelligence Artificial neural networks Back propagation networks Deep learning Electromagnetic radiation Energy Machine learning Mathematical models Meteorological data Methods Neural networks Optimization Power plants Real time Receiving Root-mean-square errors Sensors Signal reception Signal strength Turbines Weather forecasting Wind power Wireless communications Wireless networks Wireless sensor networks
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Title	Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning
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