PV Power Forecasting in the Hexi Region of Gansu Province Based on AP Clustering and LSTNet

• Published in: International transactions on electrical energy systems Vol. 2024; pp. 1 - 17
• Main Authors: Li, Xujiong, Yang, Guoming, Gou, Jun
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2024; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Affinity; Algorithms; Cluster analysis; Clustering; Correlation coefficient; Correlation coefficients; Forecasting; Machine learning; Meteorological data; Neural networks; Photovoltaic cells; Photovoltaics; Power plants; Power supply; Prediction models; Spacetime; Statistical methods; Time series; Gansu China; China
• DOI: 10.1155/2024/6667756
• ISSN: 2050-7038

Accurate PV power forecasting is becoming a mandatory task to integrate the PV system into the power grid, schedule it, and ensure the safety of the power grid. In this paper, a novel model for PV power prediction using AP-LSTNet has been proposed. It consists of a combination of affinity propagation clustering and long-term and short-term time series network models. First, the affinity propagation algorithm is used to divide the regionally distributed photovoltaic station clusters into different seasons. The Pearson correlation coefficient is used to determine the strong correlation between meteorological factors of photovoltaic power, and the bilinear interpolation method is used to encrypt the meteorological data of the corresponding photovoltaic station cluster. Furthermore, LSTNet is used to mine the long-term and short-term temporal and spatial dependence of photovoltaic power, and meteorological factor series and linear components of auto-regression are superimposed to realize the simultaneous prediction of multiple photovoltaic stations in the group. Finally, PV power plants in five cities, Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan in the Hexi region of Gansu Province, China, will be selected to test the proposed model. The experimental comparison shows that the prediction model achieves high prediction accuracy and robustness.