Abstract
Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.
Highlights
Power load forecasting is the basis for power system to arrange power generation plan, economically dispatch the grid, and determine spare capacity, and a high forecasting accuracy plays an important role in the safe and reliable operation of the power grid [1]
While the nonparametric model method overcomes these weaknesses, and it can adapt to electrical load forecasting with nonlinearity, uncertainty and time‐varying nature [6]. e methods based on nonparametric models mainly include wavelet analysis method [7, 8], grey model method [9], support vector machine
In order to resolve the endpoint e ect caused by empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) and make full use of the multi‐frequency and volatility of power system load, this paper uses variational mode decomposition (VMD) to decompose the power system load data
Summary
Power load forecasting is the basis for power system to arrange power generation plan, economically dispatch the grid, and determine spare capacity, and a high forecasting accuracy plays an important role in the safe and reliable operation of the power grid [1]. Literature [22] uses VMD to decompose the power load data and predict the individual sub‐components using the extreme learning machine (ELM), and obtain the final power load prediction value by integrating the individual sub‐components These two literatures have improved the prediction accuracy compared with the single prediction model, they do not consider the information loss that may occur in the decomposition process and the influence of other factors on the data. BP neural network is used to integrate the prediction results of each component to obtain the nal power system load prediction value
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