Search Results (253)

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems. Full article

This article presents a new method of parametric shaping of buildings with buffer spaces characterized by complex forms and effective thermal operation in the moderate climate of the Central Europe Plane. The parameterization of an elaborated thermal qualitative model of buildings with buffer spaces and its configuration based on computer simulations of thermal operation of many discrete models are the specific features of the method. The model uses various original building shapes and a new parametric artificial neural network (a) to automate the calculations and recording of results and (b) to predict a number of new buildings with buffer spaces characterized by effective thermal operation. The configuration of the parametric quantitative model was carried out based on the simulation results of 343 discrete models defined by means of ten independent variables grouping the properties of the building and buffer space related to their forms, materials and air circulation. The analysis performed for the adopted parameter variability ranges indicates a varied impact of these independent variables on the thermal operation of buildings located in a moderate climate. The infiltration and ventilation and physical properties of the windows and walls are the independent variables that most influence the energy savings utilized by the examined buildings with buffer spaces. The optimal values of these variables allow up to 50–60% of the energy supplied by the HVAC system to be saved. The accuracy and universality of the method will continuously be increased in future research by increasing the types and ranges of independent variables. Full article

Heating, ventilation, and air-conditioning (HVAC) systems account for the largest share of energy consumption in European Union (EU) buildings, representing approximately 40% of the final energy use and contributing significantly to carbon emissions. Latent thermal energy storage (LTES) using phase change materials (PCMs) has emerged as a promising strategy to enhance HVAC efficiency. This review systematically examines the role of latent thermal energy storage using phase change materials (PCMs) in optimizing HVAC performance to align with EU climate targets, including the Energy Performance of Buildings Directive (EPBD) and the Energy Efficiency Directive (EED). By analyzing advancements in PCM-enhanced HVAC systems across residential and commercial sectors, this study identifies critical pathways for reducing energy demand, enhancing grid flexibility, and accelerating the transition to nearly zero-energy buildings (NZEBs). The review categorizes PCM technologies into organic, inorganic, and eutectic systems, evaluating their integration into thermal storage tanks, airside free cooling units, heat pumps, and building envelopes. Empirical data from case studies demonstrate consistent energy savings of 10–30% and peak load reductions of 20–50%, with Mediterranean climates achieving superior cooling load management through paraffin-based PCMs (melting range: 18–28 °C) compared to continental regions. Policy-driven initiatives, such as Germany’s renewable integration mandates for public buildings, are shown to amplify PCM adoption rates by 40% compared to regions lacking regulatory incentives. Despite these benefits, barriers persist, including fragmented EU standards, life cycle cost uncertainties, and insufficient training. This work bridges critical gaps between PCM research and EU policy implementation, offering a roadmap for scalable deployment. By contextualizing technical improvement within regulatory and economic landscapes, the review provides strategic recommendations to achieve the EU’s 2030 emissions reduction targets and 2050 climate neutrality goals. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

Nearly zero-energy buildings (nZEBs) are central to global carbon reduction strategies, and Taiwan is actively promoting their adoption through building energy performance labeling, particularly in the retrofit of existing buildings. Under Taiwan’s nZEB framework, qualification requires both an A⁺ energy performance label and over 50% energy savings from retrofit technologies. This study proposes an integrated assessment framework for retrofitting small- to medium-sized office buildings into nZEBs, incorporating diagnostics, technical evaluation, policy alignment, and resource integration. A case study of a bank branch in Kaohsiung involved on-site energy monitoring and EnergyPlus V22.2 simulations to calibrate and assess the retrofit impacts. Lighting improvements and two HVAC scenarios—upgrading the existing fan coil unit (FCU) system and adopting a completely new variable refrigerant flow (VRF) system—were evaluated. The FCU and VRF scenarios reduced the energy use intensity from 141.3 to 82.9 and 72.9 kWh/m²·yr, respectively. Combined with rooftop photovoltaics and green power procurement, both scenarios met Taiwan’s nZEB criteria. The proposed framework demonstrates practical and scalable strategies for decarbonizing existing office buildings, supporting Taiwan’s 2050 net-zero target. Full article

This study presents a new comparative analysis of the cognitive control methods of HVAC systems that assess reinforcement learning (RL) and traditional proportional-integral-derivative (PID) control. Through extensive simulations in various building environments, we have shown that while the PID controller provides stability under predictable conditions, the RL-based control can improve energy efficiency and thermal comfort in dynamic environments by constantly adapting to environmental changes. Our framework integrates real-time sensor data with a scalable RL architecture, allowing autonomous optimization without the need for a precise system model. Key findings show that RL largely outperforms PID during disturbances such as occupancy increases and weather fluctuations, and that the preferably optimal solution balances energy savings and comfort. The study provides practical insight into the implementation of adaptive HVAC control and outlines the potential of RL to transform building energy management despite its higher computational requirements. Full article

The advancement of high-tech industries, notably in semiconductor manufacturing, pharmaceuticals, and precision instrumentation, has imposed stringent requirements on cleanroom environments, where strict control of airborne particulates, microbial presence, temperature, and humidity is essential. However, these controlled environments incur significant energy consumption, with air conditioning systems accounting for 40–60% of total usage due to high air circulation rates, intensive treatment demands, and system resistance. In light of global carbon reduction goals and escalating energy costs, improving the energy efficiency of cleanroom heating, ventilation, and air conditioning (HVAC) systems has become a critical research priority. Recent efforts have focused on optimizing airflow distribution, integrating heat recovery technologies, and adopting low-resistance filtration to reduce energy demand while maintaining stringent environmental standards. Concurrently, artificial intelligence (AI) methods, such as machine learning, deep learning, and adaptive control, are being employed to enable intelligent, energy-efficient system operations. This review systematically examines current energy-saving technologies and strategies in cleanroom HVAC systems, assesses their real-world performance, and highlights emerging trends. The objective is to provide a scientific basis for the green design, operation, and retrofit of cleanrooms, thereby supporting the industry’s transition toward low-carbon, sustainable development. Full article

Multipurpose heat pumps are devices able to provide simultaneously heating and cooling requirements. These devices concurrently provide useful thermal energy at condenser and evaporator with a single electrical energy input, potentially achieving energy savings as heat-recovery and co-generative technology. Despite their potential contribution to the energy transition goals as both renewable and energy-efficient technology, their use is not yet widespread. An application example for multipurpose heat pumps is air handlers, where cooling and reheat coils are classically fed by separate thermal generators (i.e., boiler, heat pumps, and chillers). This research aims at presenting the energy potential of multipurpose heat pumps as thermal generators of air handler units, comparing their performances with a classic separate configuration. A museum in the Mediterranean climate is selected as a reference case, as indoor temperature and relative humidity must be continuously controlled by cold and hot coils. The thermal loads at building and air handler level are evaluated through TRNSYS 17 and MATLAB 2022b, through specific dynamic models developed according to manufacturer’s data. An integrated building-HVAC simulation, on the cooling season with a one-hour timestep, demonstrates the advantages of the proposed technology. Indeed, the heating load is almost entirely provided by recovering energy at the condenser, and a 22% energy saving is obtained compared to classic separate generators. Furthermore, a sensitivity analysis confirms that the multipurpose heat pump outperforms separate generation systems across different climates and related loads, with consistently better energy performance due to its adaptability to varying heating and cooling demands. Full article

The current climate and energy crises demand innovative approaches to operating buildings more sustainably. HVAC systems, which significantly contribute to a building’s energy consumption, have been a major focus of research aimed at improving operational efficiency. However, a critical factor often overlooked is the seasonal and hourly variation in solar radiation and the resulting solar heat gain, which heats specific rooms differently depending on their orientation, type, and location within the building. This study proposes a simulation-based strategy to reduce HVAC energy use in hotels by allocating guests to rooms with more favorable thermal characteristics depending on the season. A high-resolution building energy model (BEM) was developed to represent a real 17-floor hotel tower in Madrid, incorporating detailed geometry and surrounding shading context. The model includes 439 internal thermal zones and simulates solar radiation using EnergyPlus’ Radiance module. The simulation results revealed large room-by-room differences in thermal energy demand. When applying an energetically optimized guest allocation strategy based on these simulations and using real occupancy data, potential reductions in HVAC energy demand were estimated to reach around 6% during summer and up to 20% in winter. These findings demonstrate that data-driven guest allocation, informed by physics-based building simulations, can provide substantial energy savings without requiring physical renovations or equipment upgrades, offering a promising approach for more sustainable hotel operation. Full article

The application of phase change material emulsions (PCMEs) in heating, ventilation, and air conditioning (HVAC) systems is considered to be a potential way of saving energy due to their relatively higher energy storage capacity compared with water. They are now widely used as a heat transfer media, so they are able to reduce the flow rate whilst delivering the same amount of cooling energy. In order to evaluate the energy-saving potential of the integrated PCME air conditioning system, whole-building energy simulation was carried out with the building simulation code TRNSYS. Before simulating the whole system, a mathematical model for a PCME-integrated fan coil unit was first developed and validated. A phase change material emulsion called PCE-10 was used, and the TRNSYS simulation showed that the required volumetric flow rate of phase change material emulsions was 50% less than that of water when providing the same cooling effect, which could contribute to a 7% reduction in total energy consumption. Full article

This study aims to experimentally evaluate a personal comfort system based on a radiant panel (R-PCS) that can regulate the thermal environment of the sleep zone during summer, with a focus on improving both the thermal comfort and energy efficiency of this system. To investigate thermal comfort under the coupling effect of different covering conditions and operating parameters of the R-PCS, the changing pattern of thermal environment parameters in the berth area and human skin temperature are analyzed. Then, the Predicted Mean Vote (PMV) -Predicted Percent Dissatisfied (PPD) index is employed for assessing the thermal comfort of the human body and energy-saving efficiency of the system. The results show that this system can satisfy the thermal comfort requirements of the human body in the berth area. Meanwhile, the corresponding cooling energy consumption of the R-PCS is significantly lower than that of the traditional HVAC system, indicating that the developed system has significant energy-saving potential in building design. Full article

The optimization of the operation strategy for building HVAC systems is the key to achieving energy conservation and consumption reduction in air-conditioning systems. This study proposes an online real-time control strategy for the air-conditioning water system based on the model predictive control (MPC) principle, implemented and validated on the integrated energy experimental platform. The experimental system simulates load generation and dissipation processes using a water tank, where hourly varying heating power output emulates the dynamic cooling loads of buildings. By regulating the chilled water system through different algorithms, the temperature tracking control performance and cooling supply regulation accuracy were rigorously validated. The control module was written in the Python 3.8 environment, and Niagara 4 software was used as an intermediate software to achieve data interaction and logical control with the laboratory system. The experimental results show that this algorithm can follow the hourly optimized parameters with a low overshoot in the short-term domain. Meanwhile, it can achieve the optimal control of cooling capacity and energy consumption in the long-term domain. Compared with the PID strategy, the temperature following control accuracy can be improved by 9.64%, and the cooling capacity can be saved by 6.24%. Compared with the day-ahead MPC algorithm, the temperature following control accuracy can be relatively improved by 16.52%, and the cooling capacity can be saved by 1.24%. Full article

When traditional HVAC (heating, ventilation, and air-conditioning) systems are in operation, they often run according to the designed operating conditions. In fact, they operate under part-load conditions for more than 90% of the time, resulting in energy waste. Therefore, studying the optimization and regulation of their operating conditions during operation is necessary. Given that the control set point for cooling tower outlet water temperature differentially impacts chiller and cooling tower energy consumption during system operation, optimization of this parameter becomes essential. Therefore, this study focuses on optimizing the cooling tower outlet water temperature control point in central air-conditioning systems. We propose the Hippopotamus Optimization Algorithm (HOA), a novel population-based approach, to optimize cooling tower outlet water temperature control points for energy consumption minimization. This optimization is achieved through a coupled computational methodology integrating building envelope dynamics with central air-conditioning system performance. The energy consumption of the cooling tower was analyzed for varying outlet water temperature set points, and the differences between three control strategies were compared. The results showed that the HOA strategy successfully identifies an optimized control set point, achieving the lowest combined energy consumption for both the chiller and cooling tower. The performance of HOA is better compared to other algorithms in the optimization process. The optimized fitness value is minimal, and the function converges after five iterations and completes the optimization in a single time step when run in MATLAB in only 1.96 s. Compared to conventional non-optimized operating conditions, the HOA strategy yields significant energy savings: peak daily savings reach 4.5%, with an average total daily energy reduction of 3.2%. In conclusion, this paper takes full account of the mutual coupling between the building and the air-conditioning system, providing a feasible method for the simulation and optimization of the building air-conditioning system. Full article

Despite stricter global energy codes, performance standards, and advanced renewable technologies, the building sector must accelerate its transition to zero carbon emissions. Many studies show that new buildings, especially non-residential ones, often fail to meet projected performance levels due to poor maintenance and management of HVAC systems. The application of predictive AI models offers a cost-effective solution to enhance the efficiency and sustainability of these systems, thereby contributing to more sustainable building operations. The study aims to enhance the control of a variable air volume (VAV) system using machine learning algorithms. A novel ventilation control model, AI-VAV, is developed using a hybrid extreme learning machine (ELM) algorithm combined with simulated annealing (SA) optimisation. The model is trained on long-term monitoring data from three office buildings, enhancing robustness and avoiding the data reliability issues seen in similar models. Sensitivity analysis reveals that accurate occupancy prediction is achieved with 8500 to 10,000 measurement steps, resulting in potential additional energy savings of up to 7.5% for the ventilation system compared to traditional VAV systems, while maintaining CO₂ concentrations below 1000 ppm, and up to 12.5% if CO₂ concentrations are slightly above 1000 ppm for 1.5% of the time. Full article

The use of renewables in heat production requires methods to overcome the issue of asynchronous heat load and energy production. The most effective method for analyzing the intricate thermal dynamics of an existing building is through transient simulation, utilizing real-world weather data. This approach offers a far more nuanced understanding than static calculations, which often fail to capture the dynamic interplay of environmental factors and building performance. Transient simulations, by their nature, model the building’s thermal behavior over time, reflecting the continuous fluctuations in temperature, solar radiation, and wind speed. Leveraging actual meteorological data enables the simulation model to faithfully capture system dynamics under realistic operational scenarios. This is crucial for evaluating the effectiveness of heating, ventilation, and air conditioning (HVAC) systems, identifying potential energy inefficiencies, and assessing the impact of various energy-saving measures. The simulation can reveal how the building’s thermal mass absorbs and releases heat, how solar gains influence indoor temperatures, and how ventilation patterns affect heat losses. In this paper, a household heating system consisting of an air source heat pump, PV, and buffer tank is simulated and analyzed. The 3D model accurately represents the building’s geometry and thermal properties. This virtual representation serves as the basis for calculating heat losses and gains, considering factors such as insulation levels, window characteristics, and building orientation. The approach is based on the calculation of building heat load based on a 3D model and EN ISO 52016-1 standard. The heat load is modeled based on air temperature and sun irradiance. The heating system is modeled in EBSILON professional 16.00 software for the calculation of transient 10 min time step heat production during the heating season. The results prove that a buffer tank with the right heat production control system can efficiently increase the auto consumption of self-produced PV electric energy, leading to a reduction in environmental effects and higher economic profitability. Full article