Abstract
Proper utilization of renewable energy sources in electricity production is inevitable due to the environmental concerns and global warming fight. Therefore, predictability of renewable electricity is a very significant issue for a long time. Main aim of this study, different from the literature, is to investigate the change of wind speed prediction errors for different time horizons. Different prediction time horizons (10, 30, 60, 90 and 120 minutes) were used, and the results were compared through the error measures and the regression values. The mean squared errors and the regression values vary between 0.819 and 5.570, and between 77.8% and 97.1%, respectively. The prediction error changes almost logarithmically, and the rate of change decreases with the increasing time horizon. A new analysis approach was proposed to see the change of the prediction error with time horizon. The equation, y = 1.5413 ln ( x ) - 2.7428, representing the change of the mean squared error with time horizon was obtained.
Highlights
Global temperature has been increasing continuingly for years due to global warming [1]
Utilization of renewable energy sources in electricity production is a promising way of decreasing fossil fuel usage
The increasing level of wind power penetration to the grid is an important consideration in energy planning
Summary
Global temperature has been increasing continuingly for years due to global warming [1]. Time horizon is a significant issue in wind speed predictions as studied by others. In study of Foley et al, effect of length of the time horizon on wind speed and wind power predictions was presented [42]. Wang et al studied on ANN approach to predict wind speed [43] They achieved to get better results in predicting wind speed at different time horizons. Main aim of this study is to evaluate the prediction performance of the ANN approach at different time horizons but not to develop the best ANN model to predict wind speed. Same configuration of the ANN was used to evaluate the performance of the ANN on short term wind speed predictions at different time horizons.
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