Method for predicting regional saturation capacity based on nonparametric model

Abstract

The invention discloses a method for predicting a regional saturation capacity based on a nonparametric model. The method comprises the steps of (1) establishing a nonparametric regression model, introducing a Gaussian kernel weight function, using a local polynomial estimation method to carry out estimation, and determining a mapping relation between the power demand and influence factors, (2) establishing a nonparametric cumulative model, introducing a secondary planning problem, and confirming a cumulative coefficient based on the nonparametric regression model, (3) selecting an impact factor, (4) selecting an order and a bandwidth according to the amount of collected data, and (5) combining data and substituting the data into the nonparametric regression model and the nonparametric cumulative model to predict electricity consumption and saturation power. According to the established nonparametric cumulative model, the precision of regional saturation capacity prediction is greatly improved, the computational complexity is reduced, the regional saturation capacity can be precisely predicted, and the rationality of regional vision power system planning work is improved.

Description

technical field [0001] The invention relates to a method for predicting regional saturated electricity, in particular to a method for predicting regional saturated electricity based on a non-parametric model, and belongs to the technical field of electric load forecasting. Background technique [0002] Saturation electricity forecast refers to the forecast of the electricity consumption scale of the whole society after the regional electricity demand enters the saturation stage. Prediction of saturated electricity is conducive to formulating long-term planning of local power grid, realizing efficient use of environmental resources, promoting sustainable development of smart grid and smooth development of medium and long-term power market transactions. [0003] Compared with the traditional mid- and long-term power forecasting, the time span of saturated power forecasting is relatively large, and it involves many and complex influencing factors, so its prediction is more diff...

AI Technical Summary

Problems solved by technology

At this time, the linear co-integration theory, error correction model and simple time series regression cannot reflect the dynamic adjustment relationship between power demand and external factors, which may cause large deviations in the prediction results.

Due to the complex changing law of saturated power market demand and the influence of many factors, the parameter model considers a single factor in the established model, and the prediction accuracy is difficult to guarantee

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