Analysis and Forecasting of Solar Power Generation using Machine Learning

Abstract

Current photovoltaic systems are equipped with a monitoring system. The data is recorded with a predefined time base. The operator has a large amount of data. This article is a contribution to the analysis and understanding of this data. It proposes to estimate Generation from this data using machine learning. The study was conducted at the 1Mw generation site in Miroslava, Iasi, Romania. The visualization and interpretation of the generation data is presented in this article. Then, prediction techniques are presented to obtain an estimate of the plant’s generation. These techniques are: Simple Exponential Smoothing (SES), Autoregression (AR), Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMA), Holt Winter’s Exponential Smoothing (HWES), Long Short Term Memory neural (LSTM) and Convolutional Neural Network (CNN).