{"title":"A new framework for ultra-short-term electricity load forecasting model using IVMD–SGMD two–layer decomposition and INGO–BiLSTM–TPA–TCN","authors":"","doi":"10.1016/j.asoc.2024.112311","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate forecasting of electricity loads is crucial for the development of electricity scheduling and supply services. With the increase in distributed electricity energy sources and the complexity of electricity systems, the strong volatility of load changes brings more challenges to the reliability of load forecasting. Therefore, we propose a novel ultra-short-term electricity load forecasting model using improved two-layer decomposition and an improved deep learning model with temporal pattern attention based on improved northern goshawk optimization (INGO). First, the load data were decomposed thoroughly using the two-layer decomposition model of improved variational mode decomposition (IVMD) with parameter optimization by INGO and symplectic geometry mode decomposition (SGMD) to improve the interpretability of the subsequences. Subsequently, INGO is used to optimize the parameters in bidirectional long short-term memory (BiLSTM). Temporal pattern attention (TPA) is added to BiLSTM, which extracts complex relationships from the hidden neurons of BiLSTM and selects relevant information from different time scales. After predicting and reconstructing the subsequences, the temporal convolutional network (TCN) prediction model is used to perform error correction to improve the final prediction accuracy. Because many government reports and policy information are summarized and published quarterly, to provide information support, we divide the electricity load datasets of two countries by quarters before forecasting. By performing multiple sets of experiments on the two datasets, it is demonstrated that the proposed model has high precision and robustness, and the obtained ultra-short-term electricity load forecasting results can accurately fit the load fluctuation trend.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568494624010858","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate forecasting of electricity loads is crucial for the development of electricity scheduling and supply services. With the increase in distributed electricity energy sources and the complexity of electricity systems, the strong volatility of load changes brings more challenges to the reliability of load forecasting. Therefore, we propose a novel ultra-short-term electricity load forecasting model using improved two-layer decomposition and an improved deep learning model with temporal pattern attention based on improved northern goshawk optimization (INGO). First, the load data were decomposed thoroughly using the two-layer decomposition model of improved variational mode decomposition (IVMD) with parameter optimization by INGO and symplectic geometry mode decomposition (SGMD) to improve the interpretability of the subsequences. Subsequently, INGO is used to optimize the parameters in bidirectional long short-term memory (BiLSTM). Temporal pattern attention (TPA) is added to BiLSTM, which extracts complex relationships from the hidden neurons of BiLSTM and selects relevant information from different time scales. After predicting and reconstructing the subsequences, the temporal convolutional network (TCN) prediction model is used to perform error correction to improve the final prediction accuracy. Because many government reports and policy information are summarized and published quarterly, to provide information support, we divide the electricity load datasets of two countries by quarters before forecasting. By performing multiple sets of experiments on the two datasets, it is demonstrated that the proposed model has high precision and robustness, and the obtained ultra-short-term electricity load forecasting results can accurately fit the load fluctuation trend.
期刊介绍:
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.