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Power consumption forecasting is an important part of the macro planning of the industry and energy sector, and accurate forecasting of power load is very important for power grid management and power dispatching. At present, most of the power load forecasting takes the region as the object, but residents and small and medium-sized enterprise users are the basic units of electricity consumption, and their power load forecasting is as important as regional power load forecasting. compared with the regional power load, the electricity load of residents and small and medium-sized enterprises is more uncertain and more difficult to forecast. Therefore, this study combines the adaptive spectral clustering (ASC) method with the support vector quantile regression model (SVQR) to analyze the electricity consumption behavior of smart grid users and predict the residential power load. In this paper, the grid search is used to optimize the parameters of the Gaussian kernel SVQR model (GSVQR) to predict the power load, and compare it with other algorithms. From the two error evaluation index values of MAPE and pinball loss, the prediction effect of the GSVQR model is the best. In order to effectively provide uncertain information of power load, the GSVQR algorithm is used to predict the load of ultra-high energy consumption users and medium energy consumption users at any time in the future. Extensive experimental results show that: compared with other models, the prediction accuracy of the GSVQR model is higher; and the prediction results of the GSVQR model still have high reliability. Therefore, the method used in this paper can solve the problem of uncertainty of load forecasting.
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