Wind Energy Predictions of Small-Scale Turbine Output Using Exponential Smoothing and Feed-Forward Neural Network

Zaccheus O. Olaofe
This article presents the comparisons of energy production predictions of a small-scale 40 kW wind turbine using an exponential smoothing technique and multilayer feed-forward neural network. For wind energy predictions, the developed mathematical model based on exponential smoothing was used to smoothen any seasonality arising in the time series data obtained at the site. This model was developed using three smoothing constant values of 0.20, 0.65, and 0.90, as well as a combination of a smoothing constant value of 0.90 with a seasonal adjustment factor for prediction of a small-scale wind turbine output for a period of 12 months. In addition, an energy model based on a multilayer feed-forward neural network was used to compute the energy generation of the turbine. The seasonally adjusted forecast model accurately predicted the wind energy output with the lowest forecast errors when compared to the chosen three smoothing constants. The energy forecasts obtained from the seasonal adjusted forecast model and multilayer feed-forward neural network were compared to the actual energy generation of the turbine at the considered tower height in terms of their forecast erroneous values.
Time Series Data (TSD); Smoothing and Seasonal Factor; Exponential Smoothing; Feed-Forward Neural Network (FNN), Small-Scale Wind Turbine
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