Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Abstract

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

“…Especially the modeling of causal forecasting in the big data, Multi-SVR is used to establish the forecasting model with high accuracy, and mainly used in the model to determine the development regularity of time series data [161][162][163][164]. CNN has the analysis ability of using the convolution kernel feature analysis, especially the hidden layer's awareness of the deep network information [165][166][167][168][169]. Through the establishment of a strong expression ability of nonlinear mapping, it can effectively and accurately analyze data and on the basis of the law of the development trend of the current data, and speculated the data law of development in the future [170][171][172].…”

Wind power forecasting technologies: A review

“…Hence Wind power forecasting has become one of the emerging research fields related majorly to electrical engineering. Several academicians and researchers are focused in the development of algorithms and the related tools for forecasting of wind power [2,3]. Ambitious goals are set by many nations to increase the generation of renewable energy to integrate in grid power where major contribution is expected from wind energy in order to reach these goals.…”

“…If these forecasting methods are accurate in computing amount of wind power generated is mere in future, lesser will the cost incurred in balancing the system. In case of large wind mills farms where wind power generation is in large scale, substantial savings can be implied for the owners of the wind farm increasing the overall efficiency of the system to a considerable level [2,5]. These power systems have a fundamental problem as the operators are unable to predict the schedule of generation of wind power due to its variability.…”

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“…The accuracy of positioning in an outdoor environment. In 2020, due to the improvement of energy structure, Yalong et al [21] were unable to adjust wind turbines for natural wind power in the past. They established a historical wind power database using CNN and collected long-term weather data to estimate short-term wind power using the wind power curve.…”