The possibility to accurately predict the actual tendencies in power production of renewable energy power plants brings benefits to both the plant’s management and the investors.
The effects of climate change and the inherent variability of the flow in streams where the power plants are installed emphasize the advantages of an accurate prediction.
To this aim, a forecasting tool based on Artificial Neural Networks has been developed. The algorithm considers geographical and climatic factors, as well as operational factors such as failure rates and maintenance operations. A recursive method has been applied in order to
increase the forecast accuracy; input data for the self-learning routine of the neural network are continuously updated during the operation of the power plant.
Different simulations have been carried out in order to test the proposed algorithm. Two case studies of practical applications in run-of-river hydr power plants, demonstrating the potentialities of the proposed forecasting tool, are presented in the paper. Direct applications range from an efficient planning of maintenance operations to the establishment of accurate production budgets.