## 2 Knowledge and Tools of Model Preparation
According to the results of interval forecasting, the power system decision makers can better understand the fluctuation range of future load changes, and better understand the uncertainty and risk factors that may exist in the future load when carrying out production planning, system safety analysis and other work, so as to make more reasonable decisions in time. According to the upper and lower bounds of interval prediction, the rotating reserve capacity of power system can be arranged, so as to improve the economic benefits of power system operation. Interval load forecasting can also meet the optimal unit combination, economic scheduling and optimal power flow of the power system dispatching department, which is conducive to improving the utilization rate of power generation equipment and the effectiveness of economic scheduling. Therefore, the analysis of power system load change and the study of power load interval forecasting method are helpful for decision makers to better grasp the change of data in power grid planning and other aspects, so as to achieve more scientific analysis and evaluation. ## 6 Conclusions

Precise and dependable power load forecasting not only takes an important place in power management and operation of smart grid, but also own environmental advantages as well as economic and social benefits. However, due to the complicated fluctuation of power load, its further development and utilization are greatly limited, and even may endanger the dispatching and management of power system. Most of the previous work focused on the deterministic point prediction of power load, seldom considered the other important aspect which is the interval prediction of power load, and this situation makes the prediction of power load not comprehensive. In order to fully mine and evaluate the deterministic and uncertain characteristics of power load, this study successfully developed a double forecast system, which makes up for the shortcomings of the existing research. The system is divided into two parts: the point forecasting module based on nonlinear combination and the interval forecasting module based on fuzzy clustering. It is of great importance to comprehensively discuss the predictability and modeling of load. Different from the previous work, this paper effectively designs BP neural network based on MOEA/D optimization as a new nonlinear combination mechanism, obtains the final prediction results, further improves the accuracy of point prediction, and improves the final prediction ability. On the basis of improving the prediction accuracy, the load data is divided into different categories based on fuzzy clustering, and then different intervals are constructed according to the prediction data of different categories. This method constructs different intervals according to different characteristics of data, which is an effective interval prediction method. Finally, a large number of experiments are carried out by using the quantitative index, which proves the effectiveness and superiority of the system. In addition, because the designed system has good performance, it can also be used in other load forecasting, wind power forecasting, economic forecasting and other fields. Funding acquisition, P.J. ; writing--original draft, Y.N. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant number71573034). The authors declare that there are no conflict of interest regarding the publication of this paper. ## Nomenclature

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