Open Access

ARTICLE

Short-Term Prediction of Photovoltaic Power Based on DBSCAN-SVM Data Cleaning and PSO-LSTM Model

by Yujin Liu¹, Zhenkai Zhang¹, Li Ma¹, Yan Jia^1,2,*, Weihua Yin³, Zhifeng Liu³

1 School of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China
2 Key Laboratory of Wind Energy and Solar Energy Utilization Technology, Ministry of Education, Hohhot, 010051, China
3 Production Management Department, Inner Mongolia Energy Power Generation Investment Group Co., Ltd., Hohhot, 010051, China

* Corresponding Author: Yan Jia. Email:

Energy Engineering 2024, 121(10), 3019-3035. https://doi.org/10.32604/ee.2024.052594

Received 08 April 2024; Accepted 03 June 2024; Issue published 11 September 2024

Abstract

Accurate short-term photovoltaic (PV) power prediction helps to improve the economic efficiency of power stations and is of great significance to the arrangement of grid scheduling plans. In order to improve the accuracy of PV power prediction further, this paper proposes a data cleaning method combining density clustering and support vector machine. It constructs a short-term PV power prediction model based on particle swarm optimization (PSO) optimized Long Short-Term Memory (LSTM) network. Firstly, the input features are determined using Pearson’s correlation coefficient. The feature information is clustered using density-based spatial clustering of applications with noise (DBSCAN), and then, the data in each cluster is cleaned using support vector machines (SVM). Secondly, the PSO is used to optimize the hyperparameters of the LSTM network to obtain the optimal network structure. Finally, different power prediction models are established, and the PV power generation prediction results are obtained. The results show that the data methods used are effective and that the PSO-LSTM power prediction model based on DBSCAN-SVM data cleaning outperforms existing typical methods, especially under non-sunny days, and that the model effectively improves the accuracy of short-term PV power prediction.

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Keywords

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