# Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models
## 4 Experience Result
### Conducting Small-Scale Tests  
The electricity consumption prediction method described in the previous section was trained using the daily electricity consumption of 17 retail stores over a period of 30 days. The model was then used to predict the electricity consumption for the next 7 days and compared against the actual electricity consumption during that period. The experimental results are shown in Figure 4.  
The proposed model evaluated the prediction errors of the validation data using the mean absolute percentage error (MAPE) and mean squared percentage error (MSPE). As shown in Table 4 below, the absolute error for MAPE should be approximately 12.5%, while for MSPE, it should be around 2%. These results indicate favorable predictive outcomes for the application of this method in the DSM aggregator. In the literature in Section 3[42; 43; 47], it is further elucidated that this research model, in addition to being innovative, presents a novel approach for ESCO providers. This contributes both academically and practically, offering practical recommendations for the sustainable and competitive implementation of DSM energy management practices in smart cities. Neural networks are a powerful method that falls within the scope of machine learning, and as such, they are very effective for prediction. However, the biggest issue is that they are difficult to explain. From this method, it can be observed that the electricity consumption prediction for weekdays from Monday to Friday is good, but there are significant differences in prediction for Saturday and Sunday. This is likely due to the fact that retail electricity is closely related to people's daily lives. On weekends, people tend to engage in activities such as outings, family activities, and various social needs, which leads to greater differences in prediction compared to weekdays. Overall, based on the small-scale experiments conducted in this study, the prediction model is quite close to actual practice, so it should be feasible. The aggregator has a \"strength in numbers\" effect by gathering many small users into a virtual large user for the target market, cooperating with the power company to reduce demand during peak or critical peak hours. However, because the structure of retail users \begin{table}
\begin{tabular}{c c c} \hline \hline  & **MAPE** & **MSPE** \\ \hline Forecasting model & 12.56\% & 2.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the error indicators. Figure 4: Daily electricity consumption prediction and comparison of actual power consumption in participating retail stores. is relatively diverse and there is no dedicated management mode for handling electricity consultation and management, the aggregator is the core of this innovative and sustainable DSM model, and the method of predicting user electricity consumption is a key focus of this core. The user electricity consumption prediction method centered on the aggregator provides feedback and rewards for both the electricity aggregator and the retail industry when the power supplier needs to reduce demand. This serves as a reference model for sustainable energy consumption as a business innovation. In addition, after predicting electricity consumption, when the power supplier, Taipower, needs to reduce demand, the demand can be transmitted to the aggregator, which can then evaluate the potential load reduction based on the electricity consumption prediction proposed in this study, and can respond to the power supplier. There are some research limitations within our model, as electricity prediction is calculated on a daily basis, which means each store only has 30 data points. In the future, if additional data can be collected, adopting a multi-objective model [48] may lead to even more precise results. All 17 stores produce total electricity output as a single unit at the same time. As pointed out, there are difficulties in collecting electricity data in some regions (such as Taiwan), and the amount of data is slightly insufficient.