# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 1 Introduction  
In China's \"13 th Five-Year\" energy revolution, the promotion of electrification, renewable energy utilization, and distributed energy utilization has been emphasized again. The construction of comprehensive energy system is an effective way to achieve this goal. It solves the problems ofrenewable energy utilization and energy efficiency by comprehensively utilizing various forms of energy and the difference between supply and demand. With the support of China's energy policy and social capital, the construction of comprehensive energy system is also continuously increasing [1, 2]. However, the volatility, uneven distribution and seasonal changes of renewable energy bring great challenges to the reliable supply of renewable energy power, and directly affect the economic benefits and industrial development of the power industry [3]. Accurate load forecasting can assist power planning and decision-making, and is an important means to solve the problem of reliable power supply when multi-energy coupling sources are involved [4, 5]. We need to consider its key influencing factors and model selection for load forecasting [6, 7]. For example, in the scenario of charging and discharging forecasting of electric vehicles, researchers take charging facilities, users, policies, and markets as key factors and apply cloud computing, big data, artificial intelligence, and other technologies to design forecasting models [8]. Each of these works contributed to the development of the field and provided appropriate models and algorithms. However, these models and algorithms also have certain limitations. For example, any combination of prediction algorithms cannot meet the needs of multi-energy coupling scenarios. For the multi-energy coupling scenario, we also need to consider the load influencing factors and forecasting model architecture of the multi-energy coupling scenario. However, in the existing load forecasting research results, few of them are consistent with the multi-energy coupling scenario. Therefore, we need to analyze the scene characteristics and key load influencing factors according to the development status of multi-energy coupling, and then build an appropriate load forecasting model according to the analysis result and aiming at the multi-energy coupling scenario. Comprehensive energy system is the main application scenario of multi-energy coupling technology, which has different structures and energy efficiency due to differences in energy composition, supply stability, load type, demand flexibility, and other factors [9, 10, 11, 12]. For example, in the cold-heat-electricity system proposed by [PERSON] et al., they use the mutual conversion technology of cold-heat-electricity and energy storage equipment to ensure the stable supply of regional cold, hot, and electric loads. At the same time, they also realize the absorption of part of renewable energy [13]; according to the multi-energy complementary system proposed by [PERSON] et al., which mainly focuses on renewable energy, they reduce the volatility of thermal power generation and improve the absorption capacity of new energy through adaptive adjustment of hydropower [14]. In the demand response system proposed by [PERSON] et al., they achieved 80%-90% renewable energy absorption and 18.76% energy expenditure savings through accurate control of electric heating, interactive competition strategy of supply and demand in the power retail market, and economic means [15]. There is no doubt that these research results have made important contributions to the efficiency improvement and structural optimization of integrated energy utilization. However, they have not comprehensively analyzed the overall characteristics and energy flow characteristics of multi-energy coupling scenarios from the view of supply side and demand side, and have not pointed out the impact of multi-energy coupling on the stable supply of grid energy. Many studies have been conducted on electric load forecasting.