## 2 Knowledge and Tools of Model Preparation
Then, according to the constructed confidence interval of each category, the prediction interval of each prediction value is calculated as:

\[[x_{i}-z_{1-\alpha/2}\frac{s_{j}}{\sqrt{n_{j}}},x_{i}+z_{1-\alpha/2}\frac{s_{ j}}{\sqrt{n_{j}}}] \tag{19}\]

where \(x_{i}\) is point prediction value, \(j\) is the category number of \(x_{i}\), \(s_{j}\) is standard deviation of category \(j\), \(n_{j}\) is the data number of category \(j\). **Step 3:** Sorting out the prediction results. According to the prediction interval of the above points, the final interval estimation of the power load is obtained. ## 4 Experiments and Analysis

This section introduces the application of the double forecast model and several comparison models, and divides the comparison into three experimental demonstrations. The operating environment of the experiment is: 2.60 GHz CPU, 4.00 GB RAM, Windows 7 and Matlab R2016A. Considering the random factors, to guarantee the reliability of the final results, 20 experiments are carried out each time, and the average value is taken, respectively. ### Dataset Description

From July 1, 2019 to July 31, 2019, power load data were collected from New South Wales, Queensland, South Australia and Tasmania, including four weeks of power load data in this paper. Electricity demand is collected every 30 min, with a total of 1488 data points and 48 data points per day. Among them, the data from July 1, 2019 to July 27, 2019 were chosen as training set of selected model, 27 days in total, including 1296 data points; the data from July 28, 2019 to July 31, 2019 were used as testing set, 4 days in total, including 192 data points. At the same time, the last three days data are used to decide the network structure of BP model. Training set and testing set select identical rollingforecasting technique and output one-step, two-step and three-step prediction results. The power load data from July 1, 2019 to July 31, 2019 and its statistical indicators which are minimum, maximum, mean and std., are shown in Table 2. The distribution condition of areas and dataset are presented in Figure 3.

As shown in the Table 2, the statistics of all samples, training sets and testing sets of three sites are similar. The data set is reasonable and can be chosen to testify the supreme ability of the proposed model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Numbers**} & \multicolumn{5}{c}{**Statistical Indicator**} \\ \cline{4-7}  & & & **Mean** & **Std.** & **Max** & **Min** \\ \hline \multirow{4}{*}{**Site 1**} & All samples & 1488 & 8568.51 & 1124.32 & 11379.76 & 6372.63 \\  & Training & 1296 & 8556.83 & 1114.88 & 11379.76 & 6372.63 \\  & Testing & 192 & 8647.37 & 1186.12 & 11107.04 & 6697.94 \\ \hline \multirow{4}{*}{**Site 2**} & All samples & 1488 & 5935.55 & 738.00 & 7884.64 & 4318.74 \\  & Training & 1296 & 5954.18 & 737.86 & 7884.64 & 4318.74 \\  & Testing & 192 & 5809.81 & 728.39 & 7337.13 & 4707.27 \\ \hline \multirow{4}{*}{**Site 3**} & All samples & 1488 & 1395.97 & 296.05 & 2229.02 & 611.41 \\  & Training & 1296 & 1379.04 & 292.24 & 2178.39 & 611.41 \\  & Testing & 192 & 1510.24 & 297.05 & 2229.02 & 997.92 \\ \hline \multirow{4}{*}{**Site 4**} & All samples & 1488 & 1231.81 & 138.65 & 1628.73 & 910.65 \\  & Training & 1296 & 1223.45 & 135.83 & 1611.11 & 910.65 \\ \cline{1-1}  & Testing & 192 & 1288.19 & 144.56 & 1628.73 & 987.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structure of four selected datasets in Australia. Figure 3: Description of observations in four sites. On the upper part is the geographic location obtained by data points. The lower part of the graph shows the divide method of training set and testing set. ### System Evaluation

The evaluation indexes of the designed double prediction system are introduced, including 5 indexes of point prediction and 4 indexes of interval prediction. #### 4.2.1 Point Forecasting Evaluation

Generally speaking, the evaluation criteria are not unique for the prediction system.