Neural Networks Application for Modelling of RES production

The article is focused on modeling and analyses of electricity production from renewable energy sources in a single-family house using artificial intelligence. The neural networks are one of the main instruments for modeling and forecasting of dynamic and stochastic processes. In the current research, they are implemented to modeling of the electricity production of a photovoltaic station of a single-family house with the aim to be able to analyze and predict the PV production in short and long term periods. This is very important in the current electricity systems in respect to ensure optimal energy utilization in the house and optimal facilities exploitation. In the article real data from an existing SCADA are used which makes the results close to real exploitation.