Power load frequency domain prediction method and system based on IRF and ODBSCAN

Abstract

According to the power load frequency domain prediction method and system based on the IRF and the ODBSCAN, the technical problem that an existing method is large in error can be solved. The inventionprovides an improved random forest IRF (Implanted Random Field) and ODBSCAN (Open Distributed Broadcast System Controller Area Network)-based method. The improved random forest IRF and ODBSCAN-basedmethod is based on an improved random forest IRF (Implanted Random Field) and an improved ODBSCAN (Open Distributed Broadcast System Controller Area Network). The invention relates to a frequency domain combination prediction method based on frequency domain combination prediction (ions width Noise). The method comprises the following steps of: firstly, decomposing a load by adopting EWT (EnhancedWavelet Transform) to obtain different intrinsic mode parts (IMFs); secondly, predicting by adopting a reasonable method according to the characteristics of each part; wherein IRF prediction is adopted for the low-frequency part and the intermediate-frequency part; the high-frequency parts have uncertainty, the ODBSCAN is used for clustering according to the temperature and humidity of meteorological factors, and then a processing method is selected according to the characteristics of each type of samples. And finally, superposing the prediction values of the parts to obtain a total prediction result. An experiment is carried out according to field load data of a city; the prediction results are compared with the prediction results of an EWT-IRF model, an EWT-RF (Random Forest) model andan EMD (Empirical Mode Decomposition)-IRF model respectively, so that a better prediction effect can be obtained, and the change rule of an actual load is reflected.

Description

technical field [0001] The invention relates to the technical field of electric power analysis, in particular to an electric load frequency domain prediction method and system based on IRF and ODBSCAN. Background technique [0002] In order to meet the needs of social development, large-scale renewable energy is connected to the power system, making the reasonable dispatch of electric energy more and more important. Load forecasting is the cornerstone of rational planning and operation of the power grid. Accurate load forecasting can maximize the use of electric energy and save electricity costs. [0003] At present, there are many forecasting methods in short-term load forecasting, such as time series method, linear regression analysis method, artificial neural network method, SVM (Support Vector Machine) and so on. Among them, the time series method and linear regression method rely on mathematical methods to establish forecasting models, which cannot solve the nonlinear ...

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Problems solved by technology

[0007] A power load frequency domain prediction method based on IRF and ODBSCAN proposed b

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