# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 1 Introduction
What is more, LSSVM has excellent fitting effect for data with small samples and high latitude, and is applicable to the scenarios in this paper [27; 30]. Compared with traditional forecasting methods, such as gray forecasting and time series, LSSVM has strong learning performance, and compared with emerging algorithms, such as the neural network algorithm, it has more strict data basis. In the application of LSSVM in load forecasting, [PERSON] et al. proposed a moth flame optimization least-squares support vector machine model (MFO-LSSVM) for annual load forecasting, and its forecasting error was within \(\pm 3\%\)[28]. [PERSON] et al. proposed to comprehensively optimize the least squares support vector machine by empirical decomposition, seasonal adjustment and particle swarm optimization (ESPLLSVM), and proved that this combinatorial optimization model was significantly superior to the single optimization model [29]. Current research results prove that swarm intelligence algorithm has good optimization ability for LSSVM, which can improve its forecasting accuracy. However, it is not a forecasting method aiming at multi-energy coupling scenarios, and its calculation efficiency can be improved. Adaptive fireworks algorithm (AFWA) is a new type of swarm intelligence algorithm. Its explosiveness and diversity make it highly applicable to complex scenarios such as multi-energy coupling scenario [32; 33]. In this paper, we use AFWA to optimize the LSSVM model, which as the load forecasting optimization method for multi-energy coupling scenario. For the first time, we designed a load forecasting method for multi-energy coupled scenarios, and proposed an efficient and accurate load forecasting model. Grid companies and power generation groups can benefit from this, in order to determine the optimal power planning scheme and equipment operation and maintenance scheme; At the same time, the subjects involved in the electricity market trading can use the forecast results to assist the medium- and long-term trading decisions. Specifically, this paper designs a load calculation method to meet the demand of power grid for the scenario of multi-energy coupling; The energy coupling relationship and effect under the multi-energy coupling scenario are analyzed comprehensively; and a load forecasting model of mRMR-AFWA-LSSVM for multi-energy coupling is proposed. The first section of this paper is the introduction, discusses the development of the research content; The structure of other parts of this paper is as follows: Section 2 is the demand analysis of power load for multi-energy coupling scenario, and put forward the key influence index system of load. Section 3 briefly describes mRMR algorithm, AFWA algorithm and LSSVM algorithm, and a complete load forecasting framework is constructed. Section 4 verifies the accuracy and calculation performance of the model by combining the load-related data of a certain region in southwest China, and makes a comparative analysis and brief conclusion of the model. Section 5 makes a further discussion and summarizes the full text.