Short-Term Energy Load Forecasting Model with Sample Filtering

Ruthbetha Kateule, Lucas A. Sakala, Mahadia Tunga
{"title":"Short-Term Energy Load Forecasting Model with Sample Filtering","authors":"Ruthbetha Kateule, Lucas A. Sakala, Mahadia Tunga","doi":"10.4314/tjs.v49i1.12","DOIUrl":null,"url":null,"abstract":"Short-term energy load forecasting is a crucial task in the power smart grid, which enables the power utilities to understand the future energy demands and plans to attain the demand and supply equilibrium, thereby optimizing power deployment and reducing power losses. Several techniques have been implemented to enhance energy load forecasting. However, the nonlinear nature of the data collected in the smart grid makes it difficult to attain 100% energy load forecasting accuracy. For instance, the Deep Feedforward Neural Networks model based on Input Attention Mechanism and Hidden Connection Mechanism has a mean absolute percentage error of 3.17%; model based on Sequence to Sequence Recurrent Neural Network with Attention had a mean absolute percentage error of 2.7%. The model based on Deep Recurrent Neural Networks with Levenberg–Marquardt backpropagation algorithm had a mean absolute percentage error of 0.58; and Deep Feedforward Neural Network with sample weights model had 3.22 % as root mean squared error. To improve energy load forecasting accuracy, this work proposed a model based on Deep Recurrent Neural Networks and sample filtering, which provides an exhaustive elucidation for modelling a sophisticated stochastic relationship between the input and output features. Deep Recurrent Neural Networks have proven to be good at modelling the nonlinearities in data of different fields and are mostly used in energy load forecasting to reduce forecasting error and a high degree of overfitting. Sample filtering is achieved through the use of K-Means clustering which determines the number of clusters to be used in the model. Findings from the study showed that by employing Deep Recurrent Neural Networks and sample filtering, the short-term energy load forecasting accuracy is improved in reference to mean absolute percentage error and root mean squared error of 0.31% and 1.014, respectively. As a result of the reduction in error, the energy demand and supply chain equilibrium are enhanced, thereby optimizing power deployment and reducing power losses. Keywords:  Machine learning, Neural networks, Sample filtering, Smart grid, Short-term energy forecasting","PeriodicalId":22207,"journal":{"name":"Tanzania Journal of Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tanzania Journal of Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4314/tjs.v49i1.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Short-term energy load forecasting is a crucial task in the power smart grid, which enables the power utilities to understand the future energy demands and plans to attain the demand and supply equilibrium, thereby optimizing power deployment and reducing power losses. Several techniques have been implemented to enhance energy load forecasting. However, the nonlinear nature of the data collected in the smart grid makes it difficult to attain 100% energy load forecasting accuracy. For instance, the Deep Feedforward Neural Networks model based on Input Attention Mechanism and Hidden Connection Mechanism has a mean absolute percentage error of 3.17%; model based on Sequence to Sequence Recurrent Neural Network with Attention had a mean absolute percentage error of 2.7%. The model based on Deep Recurrent Neural Networks with Levenberg–Marquardt backpropagation algorithm had a mean absolute percentage error of 0.58; and Deep Feedforward Neural Network with sample weights model had 3.22 % as root mean squared error. To improve energy load forecasting accuracy, this work proposed a model based on Deep Recurrent Neural Networks and sample filtering, which provides an exhaustive elucidation for modelling a sophisticated stochastic relationship between the input and output features. Deep Recurrent Neural Networks have proven to be good at modelling the nonlinearities in data of different fields and are mostly used in energy load forecasting to reduce forecasting error and a high degree of overfitting. Sample filtering is achieved through the use of K-Means clustering which determines the number of clusters to be used in the model. Findings from the study showed that by employing Deep Recurrent Neural Networks and sample filtering, the short-term energy load forecasting accuracy is improved in reference to mean absolute percentage error and root mean squared error of 0.31% and 1.014, respectively. As a result of the reduction in error, the energy demand and supply chain equilibrium are enhanced, thereby optimizing power deployment and reducing power losses. Keywords:  Machine learning, Neural networks, Sample filtering, Smart grid, Short-term energy forecasting
基于样本滤波的短期能源负荷预测模型
短期负荷预测是电力智能电网中的一项重要任务,它使电力公司能够了解未来的能源需求,并计划实现供需平衡,从而优化电力部署,减少电力损耗。已经实施了几种技术来增强能源负荷预测。然而,由于智能电网所采集数据的非线性特性,很难达到100%的电力负荷预测精度。例如,基于输入注意机制和隐藏连接机制的深度前馈神经网络模型的平均绝对百分比误差为3.17%;基于序列到序列递归神经网络的模型平均绝对百分比误差为2.7%。基于Levenberg-Marquardt反向传播算法的深度递归神经网络模型的平均绝对百分比误差为0.58;基于样本权重模型的深度前馈神经网络的均方根误差为3.22%。为了提高能源负荷预测的准确性,本文提出了一种基于深度递归神经网络和样本滤波的模型,该模型为模拟输入和输出特征之间复杂的随机关系提供了详尽的说明。深度递归神经网络在对不同领域的非线性数据进行建模方面已经被证明是很好的方法,它主要用于能源负荷预测,以减少预测误差和高度的过拟合。样本过滤是通过使用K-Means聚类来实现的,K-Means聚类决定了模型中要使用的聚类的数量。研究结果表明,采用深度递归神经网络(Deep Recurrent Neural Networks)和样本滤波后,短期能源负荷预测精度得到提高,平均绝对百分比误差和均方根误差分别为0.31%和1.014。由于误差的减少,能源需求和供应链的平衡得到了增强,从而优化了电力部署,减少了电力损失。关键词:机器学习,神经网络,样本滤波,智能电网,短期能源预测
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信