Using neural networks to forecast renewable energy resources

This contribution presents the application of feed-forward neural networks to the problem of time series forecasting. This forecast technique is applied to the water flow and wind speed time series. The results obtained from the forecasting of these two renewable resources can be used to determine the power generation capacity of micro or mini-hydraulic plants, and wind parks, respectively. The forecast values obtained with the neural network are compared against the original time series data in order to show the precision of this forecast technique.

Forecast techniques; Neural networks; Renewable energy; Time series Forecast techniques; Generation capacity; Renewable energies; Renewable resource; Time series forecasting; Time-series data; Water flows; Wind park; Wind speed; Neural networks; Parks; Time series; Wind power; Forecasting

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