Short-term prediction of photovoltaic power generation using Gaussian process regression (Papers Track)

Yahya Hasan Al Lawati (Queen Mary University of London); Jack Kelly (Open Climate Fix); Dan Stowell (Queen Mary University of London)

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Causal & Bayesian Methods


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 using 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.

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