4.2. Wind turbines generation forecasting

For forecasting simulations, we used recorded data for one small wind turbines (microgenerators) with a total power of 5 kW and another wind turbine of 10 kW, belonging to two consumers-producers (prosumers) located in two different areas of County of Tulcea, Romania. The two prosumers uses the energy produced by wind turbines for pumping water. For each turbine, the data set contains hourly data recorded from January 2013 to December 2014 with the following attributes: ambient temperature, wind direction, wind speed, atmospheric pressure and humidity. For each wind turbine, the measuring devices provides different values such as average, maximum and minimum wind speeds. Starting from the input data,


Table 5. Performance of ANN for solar generation forecasting in case of the PV panel.

we have developed several forecast scenarios by applying ARIMA models. The data recorded at the turbines' locations were analyzed using ARIMA Modeling and Forecasting time series in SAS Guide Enterprise 7.1. Applying the autoregressive model of the first AR (1), we obtained an extremely high average error (MAPE) of 86.5% for the wind turbine of 10 kW, which means that the accuracy of the model is only 13.5%. We tested also with first-order moving average (ARMA(1,1)) for both turbines and obtained the following results:


For training, validating and testing: we have allocated 70% of the records for the training process, 15% for the validation process and 15% for the testing process. For training errors, we used the mean square error (MSE) by applying an error normalization process by configuring the normalization parameter to "standard". Thus, output parameter values were stan-

Taking into account the seasonal variations of the influence factors in Romania, we built artificial neural networks based on the three algorithms for each month and we compared the

Comparing the ARIMA and ANN results, we consider that the most efficient approach is to use ANN on monthly data sets, which leads to excellent accuracy for every analyzed month. We also found that in almost 70% of cases, BR algorithm has a better generalization than LM or SCG algorithms. In 30% of cases, the highest accuracy was obtained with LM algorithm.

For forecasting simulations, we used recorded data for one small wind turbines (microgenerators) with a total power of 5 kW and another wind turbine of 10 kW, belonging to two consumers-producers (prosumers) located in two different areas of County of Tulcea, Romania. The two prosumers uses the energy produced by wind turbines for pumping water. For each turbine, the data set contains hourly data recorded from January 2013 to December 2014 with the following attributes: ambient temperature, wind direction, wind speed, atmospheric pressure and humidity. For each wind turbine, the measuring devices provides different values such as average, maximum and minimum wind speeds. Starting from the input data,

LM BR SCG LM BR SCG

January 0.0818 0.0872 0.1132 0.9994 0.9993 0.9992 February 0.0387 0.0317 0.0679 0.9994 0.9993 0.9990 March 0.0617 0.0570 0.1174 0.9985 0.9987 0.9978 April 0.0201 0.0191 0.0319 0.9990 0.9989 0.9984 May 0.0539 0.0505 0.0640 0.9980 0.9981 0.9975 June 0.0705 0.0675 0.0865 0.9991 0.9992 0.9989 July 0.0439 0.0474 0.0577 0.9967 0.9969 0.9962 August 0.0870 0.0658 0.1019 0.9991 0.9993 0.9989 September 0.0348 0.0308 0.0512 0.9997 0.9997 0.9996 October 0.0601 0.0628 0.0877 0.9996 0.9997 0.9996 November 0.0369 0.0295 0.0650 0.9999 0.9999 0.9999 December 0.1002 0.0839 0.1091 0.9997 0.9997 0.9997

Period MSE Coefficient R

Table 5. Performance of ANN for solar generation forecasting in case of the PV panel.

dardized, ranging from [�1, 1].

132 Advanced Applications for Artificial Neural Networks

4.2. Wind turbines generation forecasting

results in Table 5.

Although the moving average improves the results (especially for the 10 kW wind turbine) as a consequence of the wind's unpredictable nature, because of the very low accuracy, the ARIMA models cannot be used for forecasting wind turbines' generation.

As a consequence, we approached the feed-forward neural networks. We used the three algorithms available in Matlab for feed-forward ANN: Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG).

Considering seasonal characteristics of wind generation in Romania, where during spring and autumn the weather is windy, we split the data set into four seasons and trained and tested a dedicated ANN for each data set.

The ANN architecture was as follows: 5 neurons for the input layer (wind speed, wind direction, atmospheric pressure, temperature and humidity), 50 neurons for the hidden layer and one output (generated energy). The data set was randomly divided as follows: for training 70% of the records, for testing 15% and the remaining 15% for validation. The results obtained from each network testing and validation are synthesized in Table 6, which shows that the LM algorithm obtained the best correlation coefficient R (0.96) for spring and autumn data sets.

The results are good for all algorithms, analyzing the errors distribution we observed that most of them are between �0.1 and +0.1, which can be considered acceptable for the 5 kW turbine.

We experiment the ANN training and validation for the second turbine of 10 kW registering similar results, so we can conclude that the networks are efficient for generation forecasting in case of small wind turbines.


Table 6. Performance of ANN for wind generation forecasting in case of 5 kW turbine.
