Time Series Forecasting on Solar Irradiation using Deep Learning

Today, time series forecasting is used in various areas. Energy management is also one of the most prevalent among these areas. As a matter of fact, energy suppliers and manager have to face to the energy mix problem. Electricity can be produced from fossil fuels, from nuclear energy, from bio-fuels or from renewable energy resources. Concerning electricity generation system based on solar irradiation, it is very important to know precisely the amount of electricity available for the different sources and at different horizons: minutes, hours and days. Depending on the horizon, two main classes of methods can be used to forecast the solar irradiation: statistical time series forecasting methods for short to midterm horizons and numerical weather prediction methods for medium to long-term horizons. We focus on this report only on statistical time series forecasting methods. The aim of this study is to study if deep learning can be suitable and competitive on the solar irradiation data time series forecasting. In this context, deep learning and other machine learning methods and time series forecast studies were investigated. A special Recurrent Neural Network variations LSTM and GRU models are introduced.