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Academic Journal of Energy, 2022, 3(4); doi: 10.38007/RE.2022.030403.

Spatial Distribution of Solar Energy (SE) and Comprehensive Potential Evaluation of Regional Development and Utilization Based on Deep Learning


Tao Li, Shili Liu, Fan Yang, Yue Tang and Jianqing Li

Corresponding Author:
Tao 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

Cite This Paper

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.


[1] Bhimala K R , Gouda K C , Himesh S . Evaluating the Spatial Distribution of WRF-Simulated Rainfall, 2-m Air Temperature, and 2-m Relative Humidity over the Urban Region of Bangalore, India. Pure and Applied Geophysics, 2021, 178(1):1-16. https://doi.org/10.1007/s00024-021-02676-4

[2] Yasser, Morera-Gómez, Carlos, et al. Levels, spatial distribution, risk assessment, and sources of environmental contamination vectored by road dust in Cienfuegos (Cuba) revealed by chemical and C and N stable isotope compositions. Environmental Science and Pollution Research, 2020, 27(2):2184-2196. https://doi.org/10.1007/s11356-019-06783-7

[3] Sahin A Z , Uddin M A , Yilbas B S , et al. Performance enhancement of solar energy systems using nanofluids: An updated review. Renewable energy, 2020, 145(Jan.):1126-1148.

[4] Nirmal M , Jayaprakash P , Subramaniam U , et al. Binary Hybrid Multilevel Inverter-Based Grid Integrated Solar Energy Conversion System With Damped SOGI Control. IEEE Access, 2020, PP(99):1-1.

[5] Zeman M . Developing the future electricity grid. Europhysics News, 2021, 52(5):32-35. https://doi.org/10.1051/epn/2021505

[6] Lima M , Carvalho P , LM Fernández-Ramírez, et al. Improving solar forecasting using Deep Learning and Portfolio Theory integration. Energy, 2020, 195(Mar.15):117016.1-117016.14. https://doi.org/10.1016/j.energy.2020.117016

[7] Falahudin D , Cordova M R , Sun X , et al. The first occurrence, spatial distribution and characteristics of microplastic particles in sediments from Banten Bay, Indonesia. The Science of the Total Environment, 2020, 705(Feb.25):135304.1-135304.10.

[8] Ghatak S R , Sannigrahi S , Acharjee P . Multi-objective Framework for Optimal Integration of Solar Energy Source in Three-Phase Unbalanced Distribution Network. IEEE Transactions on Industry Applications, 2020, PP(99):1-1.

[9] Andres-Manas J A , Roca L , Ruiz-Aguirre A , et al. Application of solar energy to seawater desalination in a pilot system based on vacuum multi-effect membrane distillation. Applied Energy, 2020, 258(Jan.15):114068.1-114068.13. https://doi.org/10.1016/j.apenergy.2019.114068

[10] Liu Z , Mohammadzadeh A , Turabieh H , et al. A New Online Learned Interval Type-3 Fuzzy Control System for Solar Energy Management Systems. IEEE Access, 2021, PP(99):1-1.

[11] Boretti A . Production of hydrogen for export from wind and solar energy, natural gas, and coal in Australia. International Journal of Hydrogen Energy, 2020, 45(7):3899-3904.

[12] Sahu A , Garg A , Dixit A . A review on quantum dot sensitized solar cells: Past, present and future towards carrier multiplication with a possibility for higher efficiency. Solar Energy, 2020, 203(16):210-239. https://doi.org/10.1016/j.solener.2020.04.044

[13] Karatu A , Durmusoglu Y . Design of a solar photovoltaic system for a Ro-Ro ship and estimation of performance analysis: A case study. Solar Energy, 2020, 207(C):1259-1268.

[14] Rathore S , Park J H . A Blockchain-based Deep Learning Approach for Cyber Security in Next Generation Industrial Cyber-Physical Systems. IEEE Transactions on Industrial Informatics, 2021, 17(8):5522-5532. https://doi.org/10.1109/TII.2020.3040968

[15] Benning M , Celledoni E , Ehrhardt M J , et al. Deep learning as optimal control problems. IFAC-PapersOnLine, 2021, 54( 9):620-623. https://doi.org/10.1016/j.ifacol.2021.06.124

[16] Pollok S , Bjrk R . Deep learning for magnetism. Europhysics News, 2022, 53(2):18-21. https://doi.org/10.1051/epn/2022204

[17] Adler A , Araya-Polo M , Poggio T . Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows. IEEE Signal Processing Magazine, 2021, 38(2):89-119. https://doi.org/10.1109/MSP.2020.3037429

[18] Qamhan M A , Alotaibi Y A , Seddiq Y M , et al. Sequence-to-Sequence Acoustic-to-Phonetic Conversion Using Spectrograms and Deep Learning. IEEE Access, 2021, PP(99):1-1.