Search Results (291)

This paper presents an interpretable and modular framework for energy management in smart buildings based on fuzzy logic and the IEEE Std 1855-2016. The proposed system builds upon the JFML-IoT library, enabling the integration and execution of fuzzy rule-based systems on resource-constrained IoT devices using a lightweight and extensible architecture. Unlike conventional data-driven controllers, this approach emphasizes semantic transparency, expert-driven control logic, and compliance with fuzzy markup standards. The system is designed to enhance both operational efficiency and user comfort through transparent and explainable decision-making. A four-layer architecture structures the system into Perception, Communication, Processing, and Application layers, supporting real-time decisions based on environmental data. The fuzzy logic rules are defined collaboratively with domain experts and encoded in Fuzzy Markup Language to ensure interoperability and formalization of expert knowledge. While adherence to IEEE Std 1855-2016 facilitates system integration and standardization, the scientific contribution lies in the deployment of an interpretable, IoT-based control system validated in real conditions. A case study is conducted in a realistic indoor environment, using temperature, humidity, illuminance, occupancy, and CO₂ sensors, along with HVAC and lighting actuators. The results demonstrate that the fuzzy inference engine generates context-aware control actions aligned with expert expectations. The proposed framework also opens possibilities for incorporating user-specific preferences and adaptive comfort strategies in future developments. Full article

Energy is a critical resource, and its optimization is central to sustainable building design. Occupant comfort, significantly influenced by factors, including mean radiant temperature (MRT), alongside air temperature, velocity, and humidity, is another key consideration. This paper introduces a hybrid crow search optimization (CSA) and penguin search optimization algorithm (PeSOA), termed (HCRPN), designed to simultaneously optimize building energy consumption and achieve MRT levels conducive to thermal comfort by adjusting HVAC system parameters. We first validate HCRPN using ZDT-1 and Shaffer N1 multi-objective benchmarks. Subsequently, we employ EnergyPlus simulations, utilizing a single-objective Particle Swarm Optimization (PSO) for initial parameter analysis to generate a dataset. Following correlation analyses to understand parameter relationships, we implement our hybrid multi-objective approach. Comparative evaluations against state-of-the-art algorithms, including MoPso, NSGA-II, hybrid Nsga2/MOEAD, and Mo-CSA, validated the effectiveness of HCRPN. Our findings demonstrate an average 7% reduction in energy consumption and a 3% improvement in MRT-based comfort relative to existing methods. While seemingly small, even minor enhancements in MRT can have a noticeable positive impact on well-being, particularly in large, high-occupancy buildings. Full article

This study investigated the integration of advanced Atmospheric Water Generators (AWGs) within the design process of building energy systems, focusing on the water–energy nexus in the context of a real-life hospital building. It is based on a simulation approach, recognised as a viable means to analyse and enhance AWG potentialities. However, the current state of research does not address the issue of AWG integration within building plant systems. This study contributes to fill such a research gap by building upon an authors’ previous work and proposing an enhanced methodology. The methodology describes how to incorporate a multipurpose AWG system into the energy simulation environment of DesignBuilder (DB), version 7.0.0116, through its coupling with AWGSim, version 1.20d, a simulation tool specifically developed for atmospheric water generators. The chosen case study is a wing of the Mondino Hospital in Pavia, Italy, selected for its complex geometry and HVAC requirements. By integrating AWG outputs—covering water production, heating, and cooling—into DB, this study compared two configurations: the existing HVAC system and an enhanced version that includes the AWG as plant support. The simulation results demonstrated a 16.3% reduction in primary energy consumption (from 231.3 MWh to 193.6 MWh), with the elimination of methane consumption and additional benefits in water production (257 m³). This water can be employed for photovoltaic panel cleaning, further reducing the primary energy consumption to 101.9 MWh (55.9% less than the existing plant), and for human consumption or other technical needs. Moreover, this study highlights the potential of using AWG technology to supply purified water, which can be a pivotal solution for hospitals located in areas affected by water crises. This research contributes to the atmospheric water field by addressing the important issue of simulating AWG systems within building energy design tools, enabling informed decisions regarding water–energy integration at the project stage and supporting a more resilient and sustainable approach to building infrastructure. Full article

The optimization of ventilation strategies in high-ceiling theater stage spaces is crucial for improving thermal comfort and energy efficiency. This study addresses the challenge of uneven temperature distribution and airflow stagnation in stage environments by employing computational fluid dynamics (CFD) simulations to evaluate the effectiveness of different ventilation modes, including natural, mechanical, and hybrid systems. Six airflow organization scenarios were designed based on modifications to structural layout, equipment settings, and mechanical disturbances (e.g., fan integration). Key evaluation indicators such as temperature uniformity coefficient, airflow velocity, and exhaust efficiency were used to assess performance. The results show that a multi-dimensional optimization approach combining spatial adjustments and mechanical disturbances significantly reduced the average temperature from 26 °C to 23 °C and the temperature uniformity coefficient from 2.79 to 1.49. This study contributes a comprehensive design strategy for stage ventilation that improves comfort while minimizing energy consumption, offering practical implications for performance space design and HVAC system integration. Full article

Thermal simulations of buildings play a critical role in optimizing energy efficiency, thermal comfort, and heating, ventilation and air conditioning (HVAC) systems design. Accurate weather data is essential for reliable simulations, as local weather and climate have a significant impact on energy requirements for space heating and cooling and thermal comfort. This study conducted a literature review regarding the sources, types, and uncertainties of weather data used for thermal simulations of buildings, including typical meteorological years (TMYs) and extreme weather files under current and future climates. Additionally, this paper evaluates methods for weather data processing, including interpolation, downscaling, and synthetic generation, to improve simulation accuracy. Finally, approaches are proposed for constructing weather files for the future and extreme conditions under a changing climate. This review aims to provide a guide for researchers and practitioners to enhance the reliability of thermal modeling through informed construction, selection, and application of weather data. 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

This research study proposes an indoor temperature regulation predictive optimal control system that entails the use of both deep reinforcement learning and the Modbus TCP communication protocol. The designed architecture comprises distributed sub-parts, namely, distributed room-level units as well as a centralized main-part AI controller for maximizing efficient HVAC management in single-family residences as well as small-sized buildings. The system utilizes an LSTM model for forecasting temperature trends as well as an optimized control action using an envisaged DQN with predicted states, sensors, as well as user preferences. InfluxDB is utilized for gathering real-time environmental data such as temperature and humidity, as well as consumed power, and storing it. The AI controller processes these data to infer control commands for energy efficiency as well as thermal comfort. Experimentation on an NVIDIA Jetson Orin Nano as well as on a Raspberry Pi 4 proved the efficacy of the system, utilizing 8761 data points gathered hourly over 2023 in Cheonan, Korea. An added hysteresis-based mechanism for controlling power was incorporated to limit device wear resulting from repeated switching. Results indicate that the AI-based control system closely maintains target temperature setpoints with negligible deviations, affirming that it is a scalable, cost-efficient solution for intelligent climate management in buildings. Full article

The building sector, responsible for approximately 40% of global energy consumption, is increasingly embracing nearly zero-energy buildings (NZEBs) to promote environmental sustainability. Focusing specifically on single-family houses, this review systematically examines current NZEB practices across Europe, aiming to identify regional adaptation strategies and highlight performance disparities. The primary research question explored is as follows: how do design strategies, renewable energy integration, and climate adaptation measures for single-family NZEBs vary across Northern, Eastern, Southern, and Western European countries? A key gap in the literature is the lack of cross-comparative analysis of regional NZEB approaches for single-family houses, despite their significant share in Europe’s housing sector. Effective NZEB implementation depends on interdisciplinary collaboration among architects, engineers, and energy experts to optimize building design elements, including orientation, envelope insulation, and HVAC systems, tailored to regional climatic conditions. A systematic analysis of case studies was conducted, synthesizing data on primary energy consumption, CO₂ emissions, and building envelope performance. The findings reveal regional differences: Northern Europe exhibits primary energy consumption at 27–68 kWh/(m²·y) (mean: 48.2), Eastern Europe at 29–68 (mean: 42.5), Southern Europe at 35–42 (mean: 39.1), and Western Europe at 27–85 (mean: 51.5), with higher emissions in Eastern Europe compared to Denmark, for instance. These patterns underscore the role of climatic conditions and regulatory frameworks of the regions in shaping NZEB strategies. Despite shared goals of decarbonization and occupant comfort, significant knowledge gaps remain, particularly regarding long-term operational performance and regional comparison of other building types. Full article

Heating, ventilation, and air conditioning (HVAC) systems and microgrids have garnered significant attention in recent research, with temperature control and renewable energy integration emerging as key focus areas in urban distribution power systems. This paper proposes a robust predictive temperature control (RPTC) method and a microgrid control strategy incorporating asymmetrical challenges, including uneven power load distribution and uncertainties in renewable outputs. The proposed method leverages a thermodynamics-based R-C model to achieve precise indoor temperature regulation under external disturbances, while a multisource disturbance compensation mechanism enhances system robustness. Additionally, an HVAC load control model is developed to enable real-time dynamic regulation of airflow, facilitating second-level load response and improved renewable energy accommodation. A symmetrical power tracking and voltage support secondary controller is also designed to accurately capture and manage the fluctuating power demands of HVAC systems for supporting operations of distribution power systems. The effectiveness of the proposed method is validated through power electronics simulations in the Matlab/Simulink/SimPowerSystems environment, demonstrating its practical applicability and superior performance. Full article

Heating, ventilation, and air conditioning (HVAC) systems account for 60% of the energy consumption in commercial buildings. Each year, millions of dollars are spent on electricity bills by commercial building operators. To address this energy consumption challenge, a predictive model named Bayesian optimisation Convolution Neural Network Multivariate Long Short-term Memory (BO CNN-M-LSTM) is introduced in this research. The proposed model is designed to perform load forecasting, optimizing energy usage in commercial buildings. The CNN block extracts local features, whereas the M-LSTM captures temporal dependencies. The hyperparameter fine tuning framework applied Bayesian optimization to enhance output prediction by modifying model properties with data characteristics. Moreover, to improve occupant well-being in commercial buildings, the thermal comfort adaptive model developed by de Dear and Brager was applied to ambient temperature in the preprocessing stage. As a result, across all four datasets, the BO CNN-M-LSTM consistently outperformed other models, achieving an 8% improvement in mean percentage absolute error (MAPE), 2% in normalized root mean square error (NRMSE), and 2% in R² score.This indicates the consistent performance of BO CNN-M-LSTM under varying environmental factors, highlight the model robustness and adaptability. Hence, the BO CNN-M-LSTM model is a highly effective predictive load forecasting tool for commercial building HVAC systems. Full article

This study addresses the freeze-up problem in HVAC system heat exchangers of industrial buildings in severely cold regions by proposing a collaborative anti-freeze control strategy based on multi-objective optimization. Taking a diesel engine laboratory as the research case, key freezing-inducing factors were identified through system performance analysis and fault diagnosis. An innovative interlocked anti-freeze control system was developed by integrating electric heating with dynamic regulation of bypass air volume. Utilizing gray relational analysis, the optimal interlock control scheme was selected from four alternatives based on a comprehensive performance evaluation. Multi-objective optimization through the NSGA-II algorithm was performed on parameters including the set temperature, water flow rate, and fresh air volume, achieving coordinated optimization of energy consumption (11.4% reduction compared to pre-optimization) and thermal comfort. TRNSYS-based simulation verification demonstrated that the system maintains a 94.71% freeze protection time assurance rate under extreme operating conditions, effectively resolving the reliability deficiencies of traditional solutions in severely cold environments. This research provides a novel method for industrial building HVAC system anti-freeze design that harmonizes energy efficiency and comfort performance. 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

This study investigates the mechanism behind the cavitation-induced noise in an automotive heater core and proposes a structural solution to eliminate it. Abnormal noise during cold-start conditions in a compact passenger vehicle was traced to cavitation in the heater core of the heating, ventilation, and air conditioning (HVAC) system. Controlled bench tests, in-vehicle measurements, and computational fluid dynamics (CFD) simulations were conducted to analyze flow behavior and identify the precise location and conditions for cavitation onset. Results showed that high flow rates and low coolant pressure generated vapor bubbles near the junction of the upper tank and outlet pipe, producing distinctive impulsive noise and vibration signals. Flow visualization using a transparent pipe and accelerometer data confirmed cavitation collapse at this location. CFD analysis indicated that the original geometry created a high-velocity, low-pressure region conducive to cavitation. A redesigned outlet with a tapered transition and larger diameter significantly improved flow conditions, raising the cavitation index and eliminating cavitation events. Experimental validation confirmed the effectiveness of the modified design. These findings contribute to improving the acoustic performance and reliability of automotive HVAC systems and offer broader insights into cavitation mitigation in fluid systems. Full article

This research presents an advanced autonomous HVAC control system tailored for a chemical fiber factory, emphasizing the human-centric principles and collaborative potential of Industry 5.0. The system architecture employs several functional levels—actuator and sensor, process, model, critic, fault detection, and specification—to effectively monitor and predict indoor air pressure differences, which are critical for maintaining consistent product quality. Central to the system’s innovation is the integration of digital twins and physical AI, enhancing real-time monitoring and predictive capabilities. A virtual representation runs in parallel with the physical system, enabling sophisticated simulation and optimization. Development involved custom sensor kit design, embedded systems, IoT integration leveraging Node-RED for data streaming, and InfluxDB for time-series data storage. AI-driven system identification using Nonlinear Autoregressive with eXogenous inputs (NARX) neural network models significantly improved accuracy. Crucially, incorporating airflow velocity data alongside AHU output and past pressure differences boosted the NARX model’s predictive performance (R² up to 0.9648 on test data). Digital twins facilitate scenario testing and optimization, while physical AI allows the system to learn from real-time data and simulations, ensuring adaptive control and continuous improvement for enhanced operational stability in complex industrial settings. Full article

Personal Comfort Systems (PCS) have emerged as a flexible alternative to address the diversity of environmental perceptions in office environments. Unlike conventional HVAC systems, PCSs allow users to improve their satisfaction and comfort by exercising individualized control over their immediate environment without interfering with others around them. This study evaluated the use of machine learning models generated by H2O AutoML to predict the use of three PCSs in four office buildings with effective occupancy. These were a thermal wristband, a desk fan, and an adjustable lamp. Data collected through environmental sensors, perception surveys, and spatial and personal attributes were used. Synthetic data augmentation and automated variable selection were also used to optimize the models’ performance. The predictive models had a robust performance, with R² values in the test set of 0.86 for the wristband, 0.84 for the fan, and 0.52 for the lamp. The most influential variables included the BMI, CO₂ level, and thermal satisfaction, highlighting the importance of physiological and subjective factors. The results confirm that the models allow anticipating the use of PCS with high precision in most cases, laying the foundations for the future implementation of user-oriented adaptive systems. This preliminary approach contributes to the design of healthier, more personalized, and more energy-efficient work environments. Full article