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Research on Seasonal Rural Distribution Network Load Stochastic Model Based on Kernel Density Estimation Theory

Published: 31 July 2024 Publication History

Abstract

This article thoroughly investigates a new short-term electric load forecasting model based on non-parametric kernel density estimation. Diverging from conventional approaches, this model treats short-term electric load forecasting as a problem of nonlinear time series analysis, enabling a more accurate capture of the inherent correlations in sequential load data. It circumvents the common biases of parameter estimation methods, eliminating reliance on subjective selection of influencing factors and reducing dependence on extensive historical data. The model is capable of not only estimating load intervals but also predicting the probability of load values within specific numerical ranges, thereby providing comprehensive forecasting information for the operation and planning of electric power systems. Additionally, the model meticulously addresses the selection of model order, smoothing parameters (bandwidth), and the calculation of predictive confidence intervals. By applying two types of non-parametric kernels — Nadaraya-Watson (N-W) estimation and local polynomial estimation — this study demonstrates the effectiveness of non-parametric autoregressive models in load forecasting. The accuracy of the model is validated through the analysis of historical load data from an actual electric power system.

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  1. Research on Seasonal Rural Distribution Network Load Stochastic Model Based on Kernel Density Estimation Theory

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 31 July 2024

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