# Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models
## 2 Literature Review  
Various DSM measures have achieved outstanding results in reducing overall peak demand and peak load capacity. These DSM programs include energy efficiency measures,load management, automated control systems, and advanced metering technologies, with the residential and commercial sectors having the lowest technical and implementation costs for energy-saving and carbon reduction measures, while achieving, in relative terms, the largest energy-saving and carbon reduction effects [5,6]. This is because the residential and commercial sectors are the largest energy-consuming sectors, and implementing energy-saving and carbon reduction measures in these areas can achieve greater energy-saving and carbon reduction effects [7, 8, 9, 10, 11]. Therefore, governments, businesses, and individuals should prioritize the importance of implementing energy-saving and carbon reduction measures in the residential and commercial sectors. By providing more effective energy-saving technologies and implementation measures, environmental protection and economic benefits can be achieved while reducing energy waste. The trend in the DSM of electricity is to suppress load (with load as a capacity resource), interruptible load, direct load control, and time-of-use pricing, with time-of-use pricing and load control being the DSM schemes that can achieve the maximum benefit for the commercial sector. However, considering time-of-use pricing and load control, the biggest challenges are threefold [12, 13, 14, 15]:  
1. Retail customers should be aggregated for DSM, but it is difficult to aggregate them effectively. 2. DSM should provide forecasting tools for retail customers to consider, otherwise they will not appreciate its effectiveness, and their willingness to participate will be low. 3. The implementation of DSM largely focuses on load reduction or adjusting through time-of-use pricing. A reward-based mechanism may be a necessary approach. The commercial and industrial sectors are the two industries with the highest participation in DSM programs in the United States (Federal Energy Regulatory Commission). However, different countries and regions have different customs and practices, so before implementing such programs, each country or region should conduct surveys to understand local enterprises' awareness and willingness to adopt DSM programs. The forecasting tool for DSM should be applied to all participating users, and the service provider that aggregates all the participating users' demand is called an aggregator [16, 17, 18, 19]. The aggregator collects all users' electricity consumption data to predict electricity consumption trends. Each country or region's power companies will also have corresponding incentive systems, and the aggregator should respond to the temporary power outage incentives offered by the power company and provide DSM services that adjust users' loads in real time to achieve energy-saving and carbon-reduction policy objectives [17, 20, 21, 22]. Electricity prediction is a crucial part of DSM, and there have been many prediction models developed in the past. Predictions are seen as the foundation of management and are often used to plan future budgets, capacity, and production. As the target of the prediction becomes more unstable, the accuracy of the prediction becomes more important, such as in the case of electricity demand [23]. This review examines the application of machine learning techniques in electricity demand prediction. After comparison and analysis, the authors found that different machine learning algorithms have different levels of accuracy and stability in predicting electricity demand, and the accuracy of the model is also influenced by multiple factors. One study proposed a hybrid model based on wavelet transform and artificial intelligence technology [24] for hourly electricity demand prediction. The results showed that the model could improve the accuracy of electricity demand prediction. [PERSON] et al.