Smart grid load prediction method

Abstract

The invention relates to an intelligent power grid load prediction method, which is technically characterized by comprising the following steps of: acquiring power load data of a park for 12 months ina year, and observing the trend of the power load data; proper training data is selected for training and prediction of the Newton interpolation model; predicting power load data of the missing monthby adopting a Newton interpolation method; correcting a Newton interpolation result by adopting an after-event error estimation method; and calculating a relative error between a final Newton interpolation result and a real value, thereby finishing intelligent power grid load prediction. The method is reasonable in design, can accurately predict the short-term power consumption load condition ofthe park, is simple and easy to implement, does not need to fit a specific function form, can perform prediction and obtain a better prediction effect only through several data points, and can providea reference basis for power grid dispatching formulation and power generation and supply plans.

Description

technical field [0001] The invention belongs to the technical field of smart grids, in particular to a smart grid load forecasting method. Background technique [0002] The power load forecasting of the park is an important link in the planning of the power system. What it needs to solve is the balance between power supply and demand caused by the inability to store large-scale power, so as to ensure the quality of power supply. [0003] With the development of power system EMS, short-term load forecasting has become one of the necessary links of EMS, which provides support for the safe and economical operation of the power system. plan. Through the accurate prediction of short-term power load, it can provide a reference basis for power grid scheduling to formulate power generation and power supply plans, and ensure the balance of supply and demand of electric energy in the power grid. It can also provide data support for the production, transmission, distribution and sales...

AI Technical Summary

Problems solved by technology

[0004] At present, most of the power load forecasting methods in parks use regression analysis to establish mathematical models for power load forecasting. Due to irregular data points, this method will result in low fitting accuracy, and it is difficult to meet the precise management and control needs of smart grids.

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