Prediction of Photovoltaic Production for Smart Grid Energy Management using Hidden Markov Model: a Study Case

The increasing integration of renewable energies in the electricity grid can only be balanced with precise forecasts of future energy production. This information serves as a basis for operating and management strategies for reliable and economic integration into the power grid. Nowadays, the implementation of renewable energy forecasting methods has become an area of active research. Therefore, the ability to forecast renewable energy can play a very important role in effective network planning for renewable systems. Different approaches are used depending on the tools and data available. The aim of this work is to forecast the renewable production using the production history considered as a time series. This work deals with the use of past data to forecast future production. The emphasis in this work is on the relation between time series analysis and forecasting. First, we will look at the components of the time series. Then, we will examine some of the techniques used in analyzing data. Finally, we will predict future production using Hidden Markov Model.