# Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Trento** & **Household** & **Office** \\ \hline Batch size & 144 (144 \(\times\) 1) & 864 (144 \(\times\) 6) & 288 (288 \(\times\) 1) \\ Input & t-144 & t-144 & t-288 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters Selection. Figure 8: Data transformation. In Tables 5-7 the results of the investigated models (LSTM, CNN, MLP and ANN) for each one of the datasets are presented. The metrics utilized are the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) which are shown in the left column of each table. The CNN-based model outperforms the other ones for all three datasets. CNN achieves better results both in terms of MAE and RMSE, with the only exception being the Trento's dataset where the RMSE result of MLP is slightly better than that of CNN. The performance of CNN regarding MAE is better than that of MLP and this fact can compensate the RMSE difference while CNN also outperforms the rest of the models. The relatively small RMSE and MAE values justify the claim that the proposed batch size tuning method and the time interval input selection strategy substantially improve the performance of our methodology. Figures 9-11 present the plots of the best forecasts for Trento, household and office datasets. We have chosen to present only the last 20% of all observations from each examined load time series. This was due to two main reasons. Firstly, this part of the dataset is used for testing the algorithm and measuring accuracy of each methodology. Secondly, if we presented all observations and load time series it would be difficult for the reader to drive any conclusions. Also, we present graphically only the CNN algorithm performance since it outperforms all other solutions tested with respect to forecasting accuracy. In Figures 9-11 the actual load is depicted with red color line while the blue line represents the forecast for specific observation numbers (X axis). Y corresponds to the total load either actual or forecasted. As shown in Figures 9 and 10 the proposed model follows the actual load successfully with the exception of one spike for each dataset (where our forecast could not predict it successfully). In the household dataset, (in Figure 9) for 1900 observations our model provides a much higher value of the electric load compared to the real one. We observe the same behaviour for the office dataset ( Figure 10). For the Trento dataset (Figure 11), the CNN model performs adequately well and follows quite precisely the value of real, expected load. In general, the results indicate that our model was accurate for all datasets. Apparently, our model in all three forecasted time series exposes sufficiently good accuracy and follows satisfactorily the actual load time series. Of course, CNN cannot predict the spikes that occur randomly and are the results of sudden disturbances without any pattern and thus very difficult to be predicted. As explained earlier, CNN exploits the temporal stationarity of the load time series, hence it is not possible to predict future observations with high variability. In case of load spikes the temporal stationarity is distorted, therefore CNN cannot provide an accurate prediction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **LSTM** & **CNN** & **MLP** & **ANN** \\ \hline MAE & 0.66 & 0.55 & 0.58 & 0.78 \\ RMSE & \(1.1\) & 0.93 & 0.96 & 1.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results (Household). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **LSTM** & **CNN** & **MLP** & **ANN** \\ \hline MAE & 114.8 & 108.29 & 140.55 & 172.38 \\ RMSE & 189.38 & 180.54 & 224.96 & 236 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results (Office). Figure 10: Actual and forecasted load (office). The actual load is depicted with red color and the forecasted time series with blue color. Figure 9: Actual and forecasted load (household). The actual load is depicted with red color and the forecasted time series with blue color.