Electric power load short-term forecasting method, model, device and system

Abstract

The invention discloses a power load short-term forecasting method, a model, a device and a system. Receiving influencing factor data, wherein the influencing factor data comprises air temperature data, holiday data and industry data, and quantifying the influencing factor by a quantization method corresponding to the load data; Wavelet decomposition is used to process the load data, and four sequences are obtained by de-noising and feature extraction. The correlation analysis is carried out with the data of influencing factors, and the set of correlation pairs of load characteristics and influencing factors is obtained. The four sequences obtained from the load data are preliminarily predicted by three exponential smoothing algorithms. The set of correlations between load characteristicsand influencing factors is predicted by ARIMA-GARCH method so that adjustment factors are obtained, and the preliminary prediction results and adjustment factors are input into long-term and short-term memory network, so final prediction results are obtained.

Description

technical field [0001] The disclosure belongs to the technical field of power systems, and relates to a short-term electric load forecasting method, model, device and system. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Electricity is the lifeblood of the national economy, and it plays a vital role in economic construction, national security, and social stability. In recent years, with the in-depth development of the power market reform, the power consumption and load of power users have higher and higher requirements on the quality of power delivered by the power system. Since electric energy cannot be stored in large quantities, it is required that the output of power generation should maintain a dynamic balance with changes in electricity and load. Otherwise, it will affect the quality of power supply, and endanger the safety and ...

AI Technical Summary

Problems solved by technology

The accuracy of load forecasting will directly affect the rationality of investment, network layout and operation. If the load and power forecasting is insufficient, the development of the power grid will not be able to meet the needs of actual development, unable to meet the normal power consumption needs of users, and may even be short of power ; On the other hand, if the load and power forecast is too high, it will lead to too many equipment that cannot be fully utilized, thus causing a waste of investment

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