# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 5 Discussion  
In this paper, we proposed a least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm to predict the power load demand in the case of multi-energy coupling. Good results were obtained in the application of this method to the multi-energy coupling scenario in A region of southwest China. This paper could be concluded and analyzed with the following aspects:  
* The power grid needs to meet the needs of power users, which will be impacted by the multi-energy coupling energy supply. The difference between the total load demand and the multi-energy coupling energy supply is the power the grid needs to provide.
* As the characteristics and energy flow of different multi-energy coupling scenarios were in great difference, we had to conclude the series of key influencing factors that affect the total load demand and multi-energy coupling supply from the energy flow of multi-energy scenarios.
* The minimal redundancy maximal relevance algorithm was applied to select the top-n critical influencing factors from the series of key influencing factors, which effectively improved the stability, accuracy, and calculation speed of the model.
* The least squares support vector machine model optimized by adaptive fireworks algorithm was used for prediction, which was more accurate than other algorithms.  
In addition, the proposed least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm had a better prediction effect than other models in terms of calculation accuracy, but its prediction accuracy still had the possibility to be improved. As shown by the application of data in A region of southwest China, the main source of error was caused by the total load demand prediction process. Thus, an important strategy to improve the prediction accuracy of the total load demand prediction results is to build a more appropriate total load demand forecasting index system. We could take temperature, precipitation, light time, and other factors into consideration and put forward a more detailed and reliable index system, which will effectively improve the accuracy of the total load demand prediction, so as to improve the load prediction calculation results for the power grid.  
Although the application of the model is subject to a variety of constraints, such as the fact that it is for multi-energy coupling scenarios, that the grid is responsible for unified dispatching, and that the grid meets users' power needs, the applicability of the model is still very wide. The model has no limits on the size of the area, natural conditions, social conditions, and so on. This is because we added the step of scenario analysis before the prediction was carried out, which helped us list all the factors that might affect the prediction result, and select the influence factors with the maximum correlation and minimum redundancy through the minimal redundancy maximal relevance model, which ensure the robustness, applicability, and accuracy of the model. This will allow the model to perform well in both southwest and northeast China, enabling power companies to accurately predict user demand.  
Conceptualization, D.L.; methodology, L.W.; validation, D.L., L.W., G.Q. and M.L.; formal analysis, M.L.; investigation, G.Q.; writing--original draft preparation, L.W. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Social Science Foundation of China (No. 19 ZDA081), and Technology Projects of China State Grid Corporation (SGJS0000 YXJS1800187). The authors declare no conflict of interest.