Title: Research of the micro grid renewable energy control system based on renewable related data mining and forecasting technology

Authors: Lin Yue; Yao-jun Qu; Yan-xia Song; Shunshoku Kanae; Jing Bai

Addresses: College of Electrical and Information Engineering, Beihua University, Jilin 132021, China ' China Petroleum Jilin Chemical Engineering Co., Ltd., Jilin 132021, China ' College of Electrical and Information Engineering, Beihua University, Jilin 132021, China ' Department of Engineering, Fukui University of Technology, Japan ' College of Electrical and Information Engineering, Beihua University, Jilin 132021, China

Abstract: The output power of renewable energy has the characteristics of random fluctuation and instability, which has a harmful effect on stability of renewable power grid and causes the problem of low utilisation ratio on renewable energy output power. Thus, this paper proposes a method to predict the output power of renewable energy based on data mining technology. Firstly, the renewable generation power prediction accuracies of three different algorithm, linear regression, decision tree and random forest, are obtained and compared. Secondly, by applying the prediction result to the power dispatch control system, grid-connected renewable power will be consumed by grid-connected load to improve the utilisation ratio of renewable power. A simulation model and experiment platform is established to verify and analyse the prediction usefulness. The experiment shows that the prediction accuracy of the random forest algorithm is the highest. The tendency of renewable energy output power within a period can be calculated by using data mining technology, and the designed experiment platform system can adjust the working state automatically by following the instruction from the data mining result, which can increase the utilisation ratio of renewable energy output power and improve the stability of renewable power grid.

Keywords: data mining; micro grid; renewable energy.

DOI: 10.1504/IJCSE.2021.117021

International Journal of Computational Science and Engineering, 2021 Vol.24 No.4, pp.385 - 397

Received: 14 May 2020
Accepted: 24 Aug 2020
Published online: 12 Aug 2021 *

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