Short-time photovoltaic power forecasting based on Informer model integrating Attention Mechanism

Abstract

Precise Photovoltaic Power Generation Forecasting (PVGF) is significant for achieving reliable power supply, optimizing energy scheduling, and responding to changing energy market demand for sustainable development. However, Photovoltaic Power (PV) is vulnerable to changes in solar radiation levels and temperature, then result in electricity generation fluctuations. To further enhance the precision of PVGF, we propose a new short-term PVGF method based on Informer model integrating attention mechanism. Firstly, Locally Weighted Scatterplot Smoothing (LOWESS) is introduced to preprocess data, enhancing the stability of the input data. Secondly, Feature Engineering (FE) is used for feature screening. Thirdly, Informer model is improved, termed as Attention-Informer-Attention (AT-Informer-AT) model. Specifically, Attention mechanism (AM) layer is added to the encoder and decoder of Informer model respectively, allowing the model to flexibly adjust the attention to different time series data and effectively capture important patterns in the PV data, thereby enhancing prediction performance and generalization ability. Eventually, the novel prediction approach’s efficiency is confirmed through analyzing the cases of two different power stations in DKASC area, Alice Springs, Australia and Xuhui District, Shanghai, China. The Experimental results demonstrate that the proposed method superiors other models, with the best prediction accuracy and generalization ability.