A Power Forecasting Approach for PV Plant based on Irradiance Index and LSTM

Abstract

A novel power forecasting approach for PV plant based on irradiance index and LSTM is presented in this paper. Firstly, we come up with a clustering algorithm according to the irradiance index after analyzing the periodic characteristics of PV plant daily power curves. Then, the Long Short-Term Memory (LSTM) is employed to build forecasting models for each type of weather. An empirical study on a real dataset shows that the proposed method can effectively use multivariate time series information to predict the power for PV plants and obtain better performance than Extreme Learning Machine (ELM) and Artificial Neural Networks (ANN).