Multi-view deep forecasting for hourly solar irradiance with error correction

Abstract

Short-term solar irradiance forecasting is crucial in managing power network operations and solar photovoltaic applications. In this paper, a Multi-view Deep Forecasting method with Error Correction (MvDF_EC) for 1-hour ahead solar forecasting is proposed. MvDF_EC comprises of the Multi-view Deep Forecasting method (MvDF) and a robust Radial Basis Function Neural Network trained via minimizing the Localized Generalization Error for compensating the solar forecasting error of MvDF. MvDF consists of three deep neural networks which learn representations of input data from different views. The three views are 1) the hierarchical local temporal information extracted by the Temporal Convolutional Neural Network (TCN), 2) the key context sequential information captured by the Bi-directional Long Short-Term Memory Neural Network with Temporal Attention (BLSTMattn), and 3) long-term temporal dependencies between local temporal patterns filtered by the Convolutional Gated Recurrent Unit Neural Network (C_GRU). The solar forecasting performance of the proposed MvDF_EC is evaluated with the National Solar Radiation Database. Simulation results show that MvDF_EC yields the most accurate solar prediction compared with the benchmarks including the smart persistence and the state-of-the-art models. The lowest relative Root Mean Square Error values for Maraba and Labelle are 22.08% and 27.40%, respectively in 1-hour ahead solar forecasting.