Short-term power load prediction method and system based on hybrid model

Abstract

The invention discloses a short-term power load prediction method and system based on a hybrid model. According to the method, the time sequence characteristics of the high-frequency component subsequences are extracted through the LSTM prediction model, the short-term power load is predicted in cooperation with the ELM-CATBOOST mixed prediction model composed of the CATBOOST prediction model and the first ELM prediction model, original power load data are decomposed into a plurality of intrinsic mode function components through the CEEMDAN decomposition algorithm, the model prediction difficulty is reduced, and the prediction efficiency is improved. The prediction accuracy is improved; besides, an LSTM prediction model is utilized to extract time sequence features of the high-frequency component subsequences, historical power load data and original power load data of the high-frequency component subsequences are combined to jointly serve as input features of an ELM-CATBOOST hybrid prediction model, input feature dimension information is greatly enriched, the advantages of a single model are integrated by using the ELM-CATBOOST hybrid prediction model, and the prediction accuracy is improved. The method has higher robustness and accuracy, and different input features and prediction models are adopted for high and low frequency component subsequences, so that the model complexity can be reduced.

Description

technical field [0001] The present invention relates to the technical field of power systems, in particular to a method and system for short-term power load prediction based on a hybrid model. Background technique [0002] Short-term load forecasting (STLF) is to predict the power load from one hour to one week, which plays an important role in the planning, management and stable operation of the power system. Research shows that a 1% reduction in forecast error for a 10GW utility could save $1.6 million annually. Therefore, accurate short-term load forecasting plays an important role for power companies to reduce power waste, maintain a balance between supply and demand, reduce production costs, improve economic benefits, as well as dispatch management and power demand planning. [0003] Over the years, many scholars at home and abroad have been exploring and researching in the field of load forecasting, mainly using the following methods: statistical methods, machine lear...

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Problems solved by technology

[0004] Among them, the statistical method has a simple internal structure and fast convergence speed, and can handle linear prediction problems well, but the accuracy of nonlinear problems is not enough; machine learning methods of support vector machine (SVM) and extreme learning machine (ELM) can be very good Fitting nonlinear load data, but cannot extract temporal features in time series data; the machine learning method of long short-term memory neural network (LSTM) can handle time series data well, but there is a problem of single model prediction accuracy limitation; hybrid method is Compared with the single method, the existing hybrid method has certain advantages in load forecasting, but the existing hybrid method uses the same model for the decomposed subsequences of power load data, and different subsequences often have different characteristic information, which is not conducive to feature Information Extraction and Improving Load Forecasting Performance

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