# Decision Support Application for Energy Consumption Forecasting
## 4 Case Study  
This section presents a case study in order to evaluate the performance of the proposed application, by assessing the accuracy of the proposed forecasting methods namely ANN [16], SVM [40], HyFIS [31], WM [33], GFS.FR.MOGUL [36] and verify the influence of using different training strategies on the performance of the forecasting process. This case study includes the hour-ahead prediction of the energy consumption of GECAD's building N during 24 h of a business day. The electricity consumption is forecasted based on 4 forecasting strategies using 5 forecasting algorithms from the proposed application. Figure 12 represents a comparison between the forecasted electricity consumption for 24 h of 2018/4/6 using 5 forecasting methods based on the first training strategy as well as the real consumption values. In this strategy, the methods are trained by the electricity consumption of the 10 past days before the target day. In Figure 12 the real consumption value of every hour is presented by the black line. As it is visible, during the peak hours of the consumption the predicted values are mostly lower than the real value. The MAPE is used to measure the accuracy of the forecasted values. For this strategy, the most reliable values by the average error of 4.93% are calculated by SVM followed by HyFIS with 5.81% and WM with 5.99%. Also, the GFS.FR.MOGUL by 7.45% error and ANN by 9.83% are the methods by the highest error in the case of the first forecasting strategy. Figure 13 presents the forecasted results by the methods while using the second forecasting strategy to train the methods. The comparison between these results and the forecasted values by the first strategy shows that in the case of GFS.FR.MOGUL the results are the same and using the environmental template has no influence on the performance of this method and the error is still 7.45%. ANN by 9.58%, HyFIS by 5.61% and WM by 5.82% of MAPE error while using the second strategy, have a more reliable result than when the first strategy is used. The only method that has a higher error when the environmental temperature is used in the training data is SVM by the error of 5.21%; however, SVM still has the best result in the case of this strategy. Figure 11: The method creation and results presentation pages. Figure 12: Forecasted values and error of the methods using the 1 st data preparation strategy. Figure 13: Forecasted values and error of the methods using the 2 st data preparation strategy. Figure 14 includes the forecasting result of the third strategy. As can be seen in the results of Figure 14, the GFS.FR.MOGUL in the most hours, predicts a much lower value than the real value which eventuates an error of 15.8% for this method when the third strategy is used. HyFIS and WM by an average error of 6.15% and 6.35% also have a higher error for this strategy compared to the errors of the first and second ones. For ANN, the calculated error is 10% which is a better error than the error of this method when the second strategy is used but the performance of this method when the first strategy has been used, stills the best performed results by this method. In contrast, the SVM by the error of 4.34% while the third strategy is used has the best results between the forecasting methods and has the best predicted results by SVM between these forecasting strategies. As one can see in Figure 15, the predicted results by ANN when the fourth strategy is used have a high variation and, in some cases, the method predicts a much lower value than the real value. The average MAPE error for the ANN using the fourth strategy is 14.64%, which is the worst performed value by this method. As it has been proved, using the environmental temperature in the training data has no effect on the results of GFS.FR.MOGUL, and this method has the same error as the third strategy which is 15.8%. When the fourth strategy is used, the HyFIS has the best error by an average error of 4.58% followed by WM with 5.19% and SVM by 5.9%.