What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?
Keywords: Time series forecasting, multi-modal learning, solar irradiance, context-enriched learning
TL;DR: We present a novel deep learning architecture which leverages the spatio-temporal context (e.g. satellite data) around a meteorological station to improve the forecasting of solar irradiance at this station.
Abstract: The global integration of solar power into the electrical grid could have a crucial impact on climate change mitigation, yet poses a challenge due to solar irradiance variability. We present a deep learning architecture which uses spatio-temporal context from satellite data for highly accurate day-ahead time-series forecasting, in particular Global Horizontal Irradiance (GHI). We provide a multi-quantile variant which outputs a prediction interval for each time-step, serving as a measure of forecasting uncertainty. In addition, we suggest a testing scheme that separates easy and difficult scenarios, which appears useful to evaluate model performance in varying cloud conditions. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective use of solar power and the resulting reduction of CO$_{2}$ emissions.
Submission Number: 73
Loading