PV Power forecasting using machine learning techniques

Abstract

Due to the overwhelming challenge of catching up with the increasing demand of energy and the pressing need to greenify the energy sector to face the sensitive topics of climate changes and global warming, the importance of renewable energy sources experienced an impressive augment that is expected to continue. Hence Solar photovoltaic plants are widely integrated into most countries worldwide. either via grid-connection or stand-alone networks, as a result, forecasting the output power of solar systems, this constitutes the main challenge towards ensuring large-scale and seamless integration of photovoltaic systems to improve the accuracy of energy yield forecasts. However Photovoltaic (PV) power generation is prone to fluctuations and it is affected by different weather conditions. In this case, accurate forecasting provides the grid operators and power system designers with significant information to manage the power of demand and supply. This project aims to analyze and compare various machine learning based forecasting methods in terms of characteristics and performance. This comparative study of the models is done through error analysis. The accuracy is evaluated using historical weather data. In addition, this dissertation investigates the assessment of these models based on some well-known metrics. The obtained results show that some forecasting models for PV systems are more effective than others.