Forecasting Electricity Consumption for Accurate Energy Management in Commercial Buildings With Deep Learning Models to Facilitate Demand Response Programs

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dc.contributor.authorErten, Mustafa Yasin
dc.contributor.authorInanc, Nihat
dc.date.accessioned2025-01-21T16:41:13Z
dc.date.available2025-01-21T16:41:13Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractIn the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS.
dc.identifier.doi10.1080/15325008.2024.2317353
dc.identifier.endpage1651
dc.identifier.issn1532-5008
dc.identifier.issn1532-5016
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85186236174
dc.identifier.scopusqualityQ3
dc.identifier.startpage1636
dc.identifier.urihttps://doi.org/10.1080/15325008.2024.2317353
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24842
dc.identifier.volume52
dc.identifier.wosWOS:001165789500001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofElectric Power Components and Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectDemand forecasting; fuzzy control; load management; deep learning; smart grid
dc.titleForecasting Electricity Consumption for Accurate Energy Management in Commercial Buildings With Deep Learning Models to Facilitate Demand Response Programs
dc.typeArticle

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