Tao Li, Shili Liu, Fan Yang, Yue Tang and Jianqing Li
Economic and Technological Research Institute of State Grid Anhui Electric Power Co., Ltd, Hefei 230031, China
Energy is the fundamental driving force for social development and the material basis. In the social process from ancient times to the present, energy has always been closely related to it and is inseparable from time to time. With the continuous improvement of the level of human civilization, the role of energy has become more and more prominent, the scope of its influence has become wider and wider, and its role has become more and more important. The main purpose of this paper is to evaluate the spatial distribution of SE and the comprehensive potential of SE development and utilization based on deep learning algorithms. In order to more accurately and quantitatively describe the correlation between each parameter and photovoltaic power, this paper selects the Pearson correlation coefficient for quantitative analysis. Experimental research shows that irradiance and photovoltaic power have the greatest correlation, followed by temperature and wind speed. Through analysis, parameters such as irradiance, temperature, wind speed, wind direction and humidity can be selected for photovoltaic power prediction research.
Deep Learning, Solar Energy, Spatial Distribution, Potential Evaluation
Tao Li, Shili Liu, Fan Yang, Yue Tang and Jianqing Li. Spatial Distribution of Solar Energy (SE) and Comprehensive Potential Evaluation of Regional Development and Utilization Based on Deep Learning. Academic Journal of Energy (2022), Vol. 3, Issue 4: 23-32. https://doi.org/10.38007/RE.2022.030403.
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