A 5-day wind speed & power forecasts using a layer recurrent neural network (LRNN)

This article presents the long term wind speed and power output of a 40 kW wind turbine based on a layer recurrent neural network as the predictor. The forecast model utilized the levenberg marquardt back propagation (BP) algorithm with a tap delay for prediction of the wind speed and power generation at 5-min steps of up to 5 days ahead at station A. In addition, the BP algorithm was considered for prediction of the wind potential at station B using 10-min samples at the same tower height. For accuracy comparisons, the 10-min synthetic samples were generated from the sampled 5-min measurements at station A; and the wind predictions were compared with the 5-min predictions. To prepare the forecast model, a one month weather samples were obtained at the 20 m tower height on both wind stations. The first day data was used to train the model and forecast began at the second day for maximum period of 5 days. A usable total electricity generation of 1322.61 kWh using the sampled 5-min measurements, and 4485.56 kWh using the sampled 10-min measurements were predicted for the period of 30 days for the stations A and B, respectively. Using the generated synthetic samples at station A, a usable total electricity generation of 1320.55 kWh was predicted. The wind forecast shows a very small deviation between the use of the 5-min measurements, and the 10-min synthetic samples at station A. Furthermore, the forecast model was assessed to test how well the LRNN performed with the selected network parameters. A new weather sample was obtained from a remote station at a 20 m tower height to test the forecast model accuracy. The estimated errors were used to determine the closeness of the wind predictions to its acceptable or actual value at both stations. Accuracy test results using independent samples show close relationship with the validation results using the weather samples at station A.