# Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
## 4 Analysis of Examples  
As this paper studies how deep mining of user electricity behaviors increases the accuracy of load forecasting, in order to exclude the influences from other factors, parameters are set to be the same during the experiment of the whole forecasting phase, the number of neurons in hidden layer is L = 20, the activation function is \(sin\), the number of added data blocks for each time is one, the number of data contained in every data block is one, and the experiment is conducted in the MATLAB R2014b platform. This paper adopts MAPE (Mean Absolute Percent Error) as the measurement criteria for the accuracy of load forecasting, as follows:  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}} \right|\times 100\% \tag{12}\]  
where \(n\) is the number of clusters, \(y_{i}\) is the practical value of the partial system load from the \(i\)-th cluster, and \(\hat{y}_{i}\) is the forecast value of partial system load from the \(i\)-th cluster. At the same time, the maximum and average of MAPE (MAX-MAPE and MID-MAPE) are chosen within one day as the general measurement criteria for load forecasting accuracy on that day. Figure 5: The flowchart of the forecasting model based on OS-ELM (Online Sequential Extreme Learning Machine). Because this paper makes short-term load forecasting, the whole next day load is required to be forecasted at one time. Whereas, as shown in Table 1, the load values from the two hours before the forecasted time are used, and when the load after the next two hours is forecasted, the load forecast within the next two hours must be used. Because these values are forecast values instead of real values, the MAPE is accumulated, and on the whole, the MAPE curves present a rising trend. In order to verify the validity of the proposed sample construction method and forecasting model, this paper performs the following comparison experiment: A. The date type is not considered during clustering, and the typical day load of users is clustered directly after normalization. While performing load forecasting, the date type is considered to be a factor to construct the sample (add another dimension of data \(x_{13}\) into Table 1 to express the date type); B. The date type is considered during clustering, and the load forecasting is performed using the proposed model; C. The date type is considered during clustering, and the load forecasting is performed using the traditional ELM forecasting model. No clustering is also compared for all experiments; that is to say, \(K=1\). A. Using the proposed forecasting model, the variation curves of MAPE under different clustering conditions are shown in Figure 6 (without considering the date type). \(K\) is the number of clusters. From Figure 6, we can see that the MAPE of the load forecasting firstly decreases with increasing K (the decrease of the MAPE corresponds to the increase of the forecasting accuracy). With \(K=3\), MAPE is at its minimum, and then MAPE increases with increasing \(K\) (the forecasting accuracy decreases). At this time, the corresponding K, and the maximum and average MAPE of the load forecasting are shown in Table 2.  
B. Using the proposed forecasting model, the variation curves of MAPE under different clustering conditions are shown in Figure 7 (considering the date type). \begin{table}
\begin{tabular}{c c c} \hline \(K\) & **MAX-MAPE (\%)** & **MID-MAPE (\%)** \\ \hline \(K=1\) & 8.77 & 5.31 \\ \(K=2\) & 7.35 & 4.56 \\ \(K=3\) & 5.21 & 2.91 \\ \(K=4\) & 6.56 & 3.58 \\ \(K=5\) & 6.69 & 3.43 \\ \(K=6\) & 7.67 & 4.32 \\ \hline \end{tabular}
\end{table} 
Table 2: The maximum and average MAPE of the load forecasting under different clustering conditions in experiment A.  
Figure 6: The variation curves of MAPE (Mean Absolute Percent Error) under different clustering conditions in experiment A.