The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions.

Carbon dioxide emissions are an important factor in the increase of greenhouse gases in the atmosphere and climate change. Controlling and reducing carbon dioxide emissions plays an important role in combating global warming and climate change. Various national and international efforts are being carried out to reduce greenhouse gas emissions and switch to sustainable energy sources. For this reason, estimating carbon dioxide emissions in the coming years is important for determining the measures to be taken. In this study, Turkey's carbon dioxide emissions are successfully estimated using two different machine learning models. The success of the study was evaluated using three different statistical measures: R2, MSE and MAE. The R2 of decision trees was 89.4%, MSE was 0.013 and MAE was 0.011; the R2 of artificial neural networks was 92.7%, MSE was 0.009 and MAE was 0.006. When we compare the two models, it is seen that ANN is more successful than decision trees and predicts with less error.

Energy Performance Contracts (EPC) are performance-based financing mechanisms designed to improve energy efficiency and support renewable energy adoption in the public sector. This study examines the economic efficiency of a 1710.72 kWp solar power plant (SPP), implemented under an EPC at Alanya Alaaddin Keykubat University, using a regression-based analysis. The model evaluates the effects of solar radiation, investment cost, and electricity sales price on unit production cost, and its predictions were compared with actual production data. Results show the system exceeded the EPC contract target by 16.2%, producing 2,423,472.28 kWh in its first year and preventing 1168.64 tons of CO₂ emissions. The developed multiple linear regression model achieved a predictive error margin of 14.7%, confirming its validity. This study highlights the technical, economic, and environmental benefits of EPC applications in Türkiye’s public institutions and offers a practical decision-support framework for policymakers. The novelty lies in integrating a regression model with operational data and providing a comparative assessment of planned, predicted, and actual outcomes.

Methane gas emission into the atmosphere is rising due to the use of fossil-based resources in post-industrial energy use, as well as the increase in food demand and organic wastes that comes with an increasing human population. For this reason, methane gas, which is among the greenhouse gases, is seen as an important cause of climate change along with carbon dioxide. The aim of this study was to predict, using machine learning, the emission of methane gas, which has a greater effect on the warming of the atmosphere than other greenhouse gases. Methane gas estimation in Turkey was carried out using machine learning methods. The R² metric was calculated as logistic regression (LR) 94.9%, artificial neural networks (ANNs) 93.6%, and support vector regression (SVR) 92.3%. All three machine learning methods used in the study were close to ideal statistical criteria. LR had the least error and highest prediction success, followed by ANNs and then SVR. The models provided successful results, which will be useful in the formulation of policies in terms of animal production (especially cattle production) and the disposal of organic human wastes, which are thought to be the main causes of methane gas emission.

In current study, various types of electrospun scaffolds have been prepared including poly(L-lactide-co-ε-caprolactone), collagen type I, hydroxyapatite nanoparticles (HAp) and silver doped hydroxyapatite particles (Ag-HAp). ¹³C-NMR, ¹H-NMR, FT-IR, XRD, TEM and EDS analyses were performed to characterize the synthesized copolymer and nanoparticles. SEM analysis revealed that the greatest fiber diameter (761 ± 434 nm) was yielded for PLCL fibers, on the other hand, smallest diameter (151 ± 91 nm) was measured for PLCL/COL/Ag-HAp based fibers. According to contact angle test, the existence of collagen and HAp in the blend improved the hydrophilicity of the electrospun scaffolds. The highest tensile strength value (28 MPa) was determined for PLCL/COL based scaffold in the machine direction. Antibacterial efficiency of PLCL/COL/Ag-HAp scaffold was evaluated against gram-positive (Staphylococcus aureus) and gram-negative (Escherichia coli) bacteria and remarkable bactericidal effect (99.99%) has been recorded. Graphical Abstract

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMS), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMS and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMS is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions.