Forecasting solar radiation using a deep long short-term memory artificial neural network

Solar systems are widely used to mitigate the environmental impact of the energy sector and their importance has constantly increased due to the recent EU's strategy to lower the CO₂ emissions. Moreover, the newest Energy of Buildings Directive empathises the importance of producing energy from renewable sources to decrease the overall impact of buildings over the total end-use energy consumption. Generally, the systems' performances are highly correlated with the incident solar radiation and outdoor air temperature. Thus, being able to accurately forecast these two parameters represents a vital step in dimensioning and maximizing the overall energy production. This Paper presents the results obtained by implementing a deep recurrent artificial neural network (ANN) trained with on year solar radiation data harvested from the UPB campus. The time series data was modelled using a special ANN architecture – the LSTM (Long Short-Term Memory) – due to its special designed internal 'memory' which increases its capabilities of predicting temporal sequence data. The model uses sequences of 24 hours and the resulted mean squared error (mse) for both training and validation data is under 30%.