The Short Term Load Forecasting of RBF Neural Network Power System Based on Fuzzy Control

Ni, Jiangnan; Jin, Guo

doi:10.2991/icence-16.2016.28

Cited by 1 publication

(1 citation statement)

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“…It is connected by a large number of neurons with adjustable connection weights, and has the characteristics of large-scale parallel processing, distributed information storage, good self-learning ability and so on [1][2][3]. BP (Backpropagation) neural network [4,5] and RBF neural network [6][7][8] are common neural networks. BP neural network and RBF neural network are both nonlinear multilayer feedforward networks, but they are different in network structure, training algorithm, network resource utilization and approximation performance.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis

Niu

Dai

2017

Energies

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Abstract:As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational Analysis-Modified Particle Swarm Optimization-Least Squares Support Vector Machine (EMD-GRA-MPSO-LSSVM) load forecasting model is proposed. The model uses the de-noising method combining empirical mode decomposition and grey relational analysis to process the original load series and forecasts the processed subsequences by the algorithm of modified particle swarm optimization and least square support vector machine. Then, the final forecasting results can be obtained after reconstructing the forecasting series. This paper takes the Jibei area as an example to produce an empirical analysis for load forecasting. The model input includes the hourly load one week before the forecasting day and the daily maximum temperature, daily minimum temperature, daily average temperature, relative humidity, wind force, date type of the forecasting day. The model output is the hourly load of the forecasting day. The models of BP neural network, SVM (Support vector machine), LSSVM (Least squares support vector machine), PSO-LSSVM (Particle swarm optimization-Least squares support vector machine), MPSO-LSSVM (Modified particle swarm optimization-Least squares support vector machine), EMD-MPSO-LSSVM are selected to compare with the model of EMD-GRA-MPSO-LSSVM using the same sample. The comparison results verify that the short-term load forecasting model of EMD-GRA-MPSO-LSSVM proposed in this paper is superior to other models and has strong generalization ability and robustness. It can achieve good forecasting effect with high forecasting accuracy, providing a new idea and reference for accurate short-term load forecasting.

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