Short-Term Load Forecasting Using Hybrid Neural Network
Short-Term Load Forecasting Using Hybrid Neural Network
复制标题
使用混合神经网络进行短期负荷预测
DOI：
10.4018/ijamc.2021010108
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Ayaz Ahmad
中科院分区：
文献类型：
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作者：
Muhammad Nadeem;M. Altaf;Ayaz Ahmad
产生低成本电力的重要因素之一是准确预测电力消耗，称为负荷预测。负荷预测的主要目标是减小实际负荷与预测负荷之间的误差。由于负荷预测的非线性和对多变量的依赖性，传统的预测方法往往被人工智能技术所取代。提出了一种基于粒子群优化算法（PSO）和Levenberg马夸特（LM）神经网络预测模型的1 ~ 7天短期负荷预测方法，并将PSO和LM算法用于神经网络的训练过程。所提出的方法进行测试，预测负荷的新英格兰电力联营区的电网和现有的技术相比，使用平均绝对百分比误差来分析所提出的方法的性能。预测结果证实，所提出的LM和PSO为基础的神经网络计划优于现有的技术。
One of the important factors in generating low cost electrical power is the accurate forecasting of electricity consumption called load forecasting. The major objective of the load forecasting is to trim down the error between actual load and forecasted load. Due to the nonlinear nature of load forecasting and its dependency on multiple variables, the traditional forecasting methods are normally outperformed by artificial intelligence techniques. In this research paper, a robust short term load forecasting technique for one to seven days ahead is introduced based on particle swarm optimization (PSO) and Levenberg Marquardt (LM) neural network forecast model, where the PSO and LM algorithm are used for the training process of neural network. The proposed methods are tested to predict the load of the New England Power Pool region's grid and compared with the existing techniques using mean absolute percentage errors to analyze the performance of the proposed methods. Forecast results confirm that the proposed LM and PSO-based neural network schemes outperformed the existing techniques.