Wind Speed Prediction by Using Different Wavelet Conjunction Models

Abstract

Three wavelet conjunction models, wavelet-genetic programming (WGEP), wavelet-neuro-fuzzy (WNF) and wavelet-neural network (WNN) were introduced in this paper for predicting hourly and daily wind speed values with three lag times. Hourly wind speed measurements from Darwin Airport synoptic station and daily wind speed data from Tabriz Station (North-western Iran) were used as inputs to the wavelet conjunction models to predict 1-, 2- and 3-hour and 1-, 2- and 3-days ahead wind speeds. First, conventional GEP, NF and ANN models were applied to the wind speed time series. Then WGEP, WNF and WANN conjunction models were also used for the same purpose and their results were compared with those of the conventional GEP, NF and ANNs. The correlation coefficient, root mean squared error, scatter index and mean absolute error were used to evaluate the performance of the models. Inter-comparisons of model results indicated that the use of wavelet conjunction models increased the performance of the conventional GEP, ANFIS and ANN in forecasting hourly and daily wind speeds.