Decomposition spectral graph convolutional network based on multi-channel adaptive adjacency matrix for renewable energy prediction

Abstract

The intermittence and randomicity of renewable energy can seriously disrupt its productivity and reliability, particularly in unforeseen circumstances. Yet, most existing renewable energy prediction (REP) methods fail to model the inter-temporal correlations among input variables and suffer from impractical decomposition process, resulting in less good prediction accuracy. Aiming to improve these issues and power REP technique to better serve modern society, this work proposes a novel decomposition spectral graph convolutional network, named DASGCN, as a general solution for REP. Compared with existing REP models, DASGCN has two main characteristics: (1) It incorporates a carefully designed decomposition block which can decompose input multivariate time series efficiently, (2) A multi-channel adaptive adjacent matrix is proposed to model the complex correlation among entangled input variables automatically and generate the interpretable graph structure without prior domain knowledge. The comprehensive experiments on four real-world datasets demonstrate that the proposed DASGCN can significantly outperform benchmark models and achieve high performance on wind speed, wind power, solar radiation and photovoltaic power prediction tasks.