A Short-Term Power Output Forecasting Model Based on Correlation Analysis and ELM-LSTM for Distributed PV System

Abstract

Accurate short-term power output forecasting results are conducive to reducing the scheduling difficulty of grid-connected operation of distributed photovoltaic (PV) systems, thus improving the safety and stability of power grid operation. In this paper, a one-day-ahead short-term power output forecasting model based on correlation analysis and combination algorithms for distributed PV system is proposed to solve the problems within the current methods. Firstly, the basic information of distributed PV system is introduced, and the main influence factors affecting the power output of distributed PV system are determined. Secondly, the influence factors with higher correlation with PV output are selected by Spearman rank-order correlation coefficient (SROCC) analysis in multiple timescales. Then, based on the multimodel univariate extreme learning machine (ELM) submodel and the single-model multivariate long short-term memory (LSTM) submodel, the ELM-LSTM model is established. The case study analysis based on the actual data indicates that the ELM-LSTM forecasting model proposed in this paper has higher forecasting accuracy than the traditional forecasting methods.