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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Veröffentlicht in:Mathematical problems in engineering Jg. 2022; S. 1 - 10
Hauptverfasser: Tian, Weihua, Bao, Yan, Liu, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 28.04.2022
John Wiley & Sons, Inc
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/7952860