Short-term prediction of photovoltaic power generation using Gaussian process regression

TL;DR

This paper evaluates Gaussian process regression for short-term PV power prediction in the UK, focusing on factors like training period, sky coverage, and cloud coverage to improve reliability amid weather uncertainty.

Contribution

It introduces a GPR-based model for PV power forecasting and assesses its performance under various conditions, highlighting the impact of sky and cloud coverage on prediction accuracy.

Findings

GPR provides useful uncertainty estimates for PV power forecasts.

Prediction accuracy varies with training period and sky coverage.

Initial cloud coverage is a significant predictor for very short-term forecasts.

Abstract

Photovoltaic (PV) power is affected by weather conditions, making the power generated from the PV systems uncertain. Solving this problem would help improve the reliability and cost effectiveness of the grid, and could help reduce reliance on fossil fuel plants. The present paper focuses on evaluating predictions of the energy generated by PV systems in the United Kingdom Gaussian process regression (GPR). Gaussian process regression is a Bayesian non-parametric model that can provide predictions along with the uncertainty in the predicted value, which can be very useful in applications with a high degree of uncertainty. The model is evaluated for short-term forecasts of 48 hours against three main factors -- training period, sky area coverage and kernel model selection -- and for very short-term forecasts of four hours against sky area. We also compare very short-term forecasts in terms of cloud coverage within the prediction period and only initial cloud coverage as a predictor.