# Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models
## 4 Experience Result
### Proposed Analysis and Prediction Methods  
Under this approach, it is not possible to predict the electricity consumption and behavior of each retail store, nor to explain the electricity consumption habits of each store. Although this may seem unfortunate, it still aligns with the original purpose of this study. The aggregator's DSM approach requires a group of retail stores to be willing to reduce their electricity consumption simultaneously. Therefore, we analyzed the electricity consumption of a group of retail stores to simulate the future scenario wherein an aggregator operator may request a reduction in electricity consumption from this group of stores. The actual reduction in electricity consumption from this group of stores indeed met the aggregator's demand for reducing electricity consumption. Whether the total reduction in electricity consumption from this group of stores meets the aggregator's (or more precisely, the power company's) demand for reducing electricity consumption can be observed through this analytical model. In the previous survey, 28 out of 56 retail stores expressed their willingness to use electricity consumption monitoring to adjust their energy usage, and 17 of them provided their business-related information for research analysis after learning about the DSM aggregator model proposed in this study. The purpose of DSM is to aggregate the total electricity consumption of each store and reduce power consumption through temporary power outages or reductions. The primary energy consumption in the retail industry comes from heating water, air conditioning,cooking, and lighting, all of which rely on electricity. To effectively reduce the electrical demand of the service industry, it is essential to actively improve the energy efficiency of various energy-consuming devices. However, based on current practical data, the study subjects targeted by this research are limited by Taiwan's existing power facilities and infrastructure, making it challenging to obtain detailed data. This means that individual data for retail businesses cannot be analyzed, and analysis and electricity predictions must be based on the total electricity consumption of each retail business. Therefore, we adopt this simpler approach as much as possible in our calculations. In our model, electricity predictions are calculated on a daily basis, meaning each store has only 30 data points. All 17 stores are considered as a single unit producing total power output at the same time. Thus, this study analyzes and predicts electricity consumption based on the total electricity consumption of each retail business. There are a total of 4 convenience stores, 2 department stores, 1 supermarket, and 1 hotel, as well as 9 catering enterprises, totaling 17 businesses, with their total electricity consumption as our parameter. As the previous experiments were based on total electricity consumption, the daily electricity consumption of each enterprise is parameter R, R: 1-17, as shown in Table 3.  
R1 represents the total electricity consumption of the first convenience store, R2 represents the total electricity consumption of the second convenience store, R5 represents the total electricity consumption of the first department store, R7 represents the total electricity consumption of the hotel, R8 represents the total electricity consumption of the first restaurant, R10 represents the total electricity consumption of the second restaurant, and so on. We observed that electricity consumption in the retail industry follows a regular cycle, which coincides with people's weekly habits, over a seven-day period. Therefore, this study uses data from the previous day up to the previous seven days as training data, specifically the total electricity consumption of retail stores during this period. The study aims to demonstrate the feasibility of aggregator's DSM approach by utilizing past electricity consumption for simple prediction.