Short-term forecasting of solar irradiance without local telemetry: a generalized model using satellite data

TL;DR

This paper presents a satellite data-based deep learning model capable of accurately forecasting short-term solar irradiance across various locations without relying on local ground measurements, facilitating broader deployment.

Contribution

A novel generalized deep neural network model that predicts solar irradiance without local data, trained on limited sites and applicable to many locations.

Findings

Model achieves comparable or better accuracy than local models.

Average 31.31% rRMSE across locations, outperforming local models.

Effective for diverse geographic locations without ground data.

Abstract

Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g.~operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain, it is very important to have general models that can predict solar irradiance without the need of local data. In this paper, a model that can perform short-term forecasting of solar irradiance in any general location without the need of ground measurements is proposed. To do so, the model considers satellite-based measurements and weather-based forecasts, and employs a deep neural network structure that is able to generalize across locations; particularly, the network is trained only using a small subset of sites where ground data is available, and the model is able to generalize to a much larger number of locations where ground data does not exist. As a case study, 25 locations in The Netherlands are considered and the proposed model is compared against four local models that are individually trained for each location using ground measurements. Despite the general nature of the model, it is shown show that the proposed model is equal or better than the local models: when comparing the average performance across all the locations and prediction horizons, the proposed model obtains a 31.31% rRMSE (relative root mean square error) while the best local model achieves a 32.01% rRMSE.