Solar energy production: Short-term forecasting and risk management

TL;DR

This paper introduces a novel approach for short-term solar energy production forecasting that uses confidence bands in time series analysis, accommodating non-Gaussian data distributions, and demonstrates its effectiveness through extensive simulations.

Contribution

It proposes a new time series framework with confidence bands that do not rely on Gaussian assumptions, enhancing the accuracy and practicality of solar energy forecasting.

Findings

Confidence bands are effective for non-Gaussian data.

The method improves short-term solar energy forecasts.

Simulations validate the approach's robustness.

Abstract

Electricity production via solar energy is tackled via short-term forecasts and risk management. Our main tool is a new setting on time series. It allows the definition of "confidence bands" where the Gaussian assumption, which is not satisfied by our concrete data, may be abandoned. Those bands are quite convenient and easily implementable. Numerous computer simulations are presented.