Machine Learning for Energy Management in Buildings: A Systematic Review on Real-World Applications

Basit Öğe Kaydı
dc.authoridKURUCAN, MEHMET/0000-0003-4359-3726
dc.authoridCOBAN, HASAN HUSEYIN/0000-0002-5284-0568
dc.contributor.authorMichailidis, Panagiotis
dc.contributor.authorMinelli, Federico
dc.contributor.authorMichailidis, Iakovos
dc.contributor.authorKurucan, Mehmet
dc.contributor.authorCoban, Hasan Huseyin
dc.contributor.authorKosmatopoulos, Elias
dc.date.accessioned2026-02-27T07:33:33Z
dc.date.available2026-02-27T07:33:33Z
dc.date.issued2025
dc.description.abstractMachine learning (ML) is becoming a key enabler in building energy management systems (BEMS), yet most existing reviews focus on simulations and fail to reflect the realities of real-world deployment. In response to this limitation, the present work aims to present a systematic review dedicated entirely to experimental, field-tested applications of ML in BEMS, covering systems such as Heating, Ventilation & Air-conditioning (HVAC), Renewable Energy Systems (RES), Energy Storage Systems (ESS), Ground Heat Pumps (GHP), Domestic Hot Water (DHW), Electric Vehicle Charging (EVCS), and Lighting Systems (LS). A total of 73 real-world deployments are analyzed, featuring techniques like Model Predictive Control (MPC), Artificial Neural Networks (ANNs), Reinforcement Learning (RL), Fuzzy Logic Control (FLC), metaheuristics, and hybrid approaches. In order to cover both methodological and practical aspects, and properly identify trends and potential challenges in the field, current review uses a unified framework: On the methodological side, it examines key-attributes such as algorithm design, agent architectures, data requirements, baselines, and performance metrics. From a practical standpoint, the study focuses on building typologies, deployment architectures, zones scalability, climate, location, and experimental duration. In this context, the current effort offers a holistic overview of the scientific landscape, outlining key trends and challenges in real-world machine learning applications for BEMS research. By focusing exclusively on real-world implementations, this study offers an evidence-based understanding of the strengths, limitations, and future potential of ML in building energy control-providing actionable insights for researchers, practitioners, and policymakers working toward smarter, grid-responsive buildings. Findings reveal a maturing field with clear trends: MPC remains the most deployment-ready, ANNs provide efficient forecasting capabilities, RL is gaining traction through safer offline-online learning strategies, FLC offers simplicity and interpretability, and hybrid methods show strong performance in multi-energy setups.
dc.description.sponsorshipHellenic Foundation for Research and Innovation (HFRI) [16880]
dc.description.sponsorshipThis research was partially funded by the SEED4AI project. The project is being implemented within the framework of the National Recovery and Resilience Plan Greece 2.0, with funding from the European Union-NextGenerationEU (Implementing body: Hellenic Foundation for Research and Innovation (HFRI))/ID: 16880. SEED4AI: https://seed4ai.ee.duth.gr/ accessed on 22 November 2025.
dc.identifier.doi10.3390/en19010219
dc.identifier.issn1996-1073
dc.identifier.issue1
dc.identifier.urihttp://dx.doi.org/10.3390/en19010219
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4634
dc.identifier.volume19
dc.identifier.wosWOS:001657341500001
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofEnergies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20260302
dc.subjectmachine learning
dc.subjectenergy management
dc.subjectsmart buildings
dc.subjectmodel predictive control
dc.subjectreinforcement learning
dc.subjectHVAC
dc.subjectRES
dc.subjectESS
dc.subjectEVCS
dc.subjectcontrol optimization
dc.titleMachine Learning for Energy Management in Buildings: A Systematic Review on Real-World Applications
dc.typeReview

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu