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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| Published in: | Mathematical problems in engineering Vol. 2022; pp. 1 - 10 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
Hindawi
28.04.2022
John Wiley & Sons, Inc |
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| 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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Weihua orcidid: 0000-0002-5460-0832 surname: Tian fullname: Tian, Weihua organization: School of AutomationShenyang Institute of EngineeringShenyangLiaoningChinasie.edu.cn – sequence: 2 givenname: Yan surname: Bao fullname: Bao, Yan organization: School of AutomationShenyang Institute of EngineeringShenyangLiaoningChinasie.edu.cn – sequence: 3 givenname: Wei surname: Liu fullname: Liu, Wei organization: School of AutomationShenyang Institute of EngineeringShenyangLiaoningChinasie.edu.cn |
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| ContentType | Journal Article |
| 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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