Artificial Intelligence-Based Prediction of Renewable Energy Sources Using Correlation Testing

Abstract

The purpose of renewable energy to generate power and change the composition of the energy mix is becoming more popular in today’s world. The competition of solar power plants in the energy market and decreasing reliance on fossil fuels for socio andeconomic growth are both facilitated by solar energy forecasting, which is a critical component. To overcome this issue, we presented an Adaptive sea lion optimized genetic adversarial network (ASLO-GAN) method. The purpose of the ASLO-GAN method is to predict the renewable energy sources. The dataset of renewable energy sources (RESs) is collected from kaggleand then the collected dataset is preprocessed using decimal scale normalization. When extracting the data, the spearman rank-order correlation (SROC) method is utilized so undesirable data can be omitted from the process. The simulation results include RMSE, MAPE, R-square, CV(RMSE) and NMBE that are evaluated. The main key findings of NMBE were compared to other traditional approaches in order to show the validity and repeatability of the inquiry.

Keywords:

• Renewable energy
• artificial intelligence (AI)
• solar energy
• fossil fuel
• Spearman Rank-Order Correlation (SROC)

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Xianglong Li

Xianglong Li was born in Beijing, China. He received the Master degree from Xi'an Jiaotong University, P.R. China. Now, he works at the Electric Power Research Institute, State Grid Beijing Electric Power Company. His research interests include power system, electric vehicles and blockchain. E-mail: [email protected]

Shangzhuo Zheng

Shangzhuo Zheng was born in Heilongjiang, China .He received the Bachelor degree from Mudanjiang Normal University, P.R. China. Now, he works at the State Grid Blockchain Technology (Beijing) Co., LTD. His research interests include power information communication and blockchaine. E-mail: [email protected]

Weixian Wang

Weixian Wang was born in Beijing, China. She received the Master degree from Beijing University of Technology, P.R. China. Now, she works at the Electric Power Research Institute, State Grid Beijing Electric Power Company. Hers research interests include blockchain, artificial intelligence and electric vehicles. E-mail: [email protected]

Lu Zhang

Lu Zhang was born in Beijing, China. He received the Doctor degree from Beijing University of Technology, P.R. China. Now, he works at the Electric Power Research Institute, State Grid Beijing Electric Power Company. His research interests include big data for Electricity, Blockchain and artificial intelligence digital technology. E-mail: [email protected]