Air-conditioned Buildings Research Articles

PMVd/PPDd model for predicting thermal comfort in air-conditioned buildings in hot and humid regions of Sub-Saharan Africa

The model for predicting thermal comfort conditions established by Fanger remains widely used in scientific work, as a default means of in situ measurements. However, the use of Fanger data leads to dissatisfaction among users of air-conditioned buildings in humid tropical areas. This conclusion emerges from the work of Kemajou and al. (Cameroon) and Olissan and al. (Benin) in 2012, after survey studies on the judgments of thermal sensations of people occupying various air-conditioned spaces. This article develops a correction model for the Fanger model, by coupling the latter to models for predicting skin temperature and heat loss by evaporation of sweat through the skin, as well as to the skin thermal sensitivity of people active in a humid tropical climate. The model thus determined shows that the predicted optimal comfort temperatures are located in the range of 26.1 °C to 27.5 °C and better meets the expectations of the populations in this region. The respective differences with the investigations of Kemajou and al. and those of Olissan and al. are 0.40 °C and 0.55 °C. It also appears that the comfort humidity range is 30% to 80%, apart from any other safety measures.

A joint optimization strategy for electric vehicles and air conditioning systems with building battery configuration

Building air conditioning systems, electric vehicles and battery energy storage systems all provide substantial flexibility for grid operations. However, the joint optimization strategy involving these three demand response resources in buildings has been infrequently studied. This research proposes a day-ahead optimization strategy to coordinate the joint operation of air conditioning systems and electric vehicles. In this framework, cooling load is predicted using an autoregressive exogenous model, while electric vehicle charging load is predicted through Monte Carlo simulations. An evaluation method is introduced to assess the comprehensive benefits of the demand response strategy, considering thermal comfort, economic efficiency, and grid friendliness. Using an office building in Tianjin, China, as a case study, the results indicate that the joint optimization strategy reduces operational costs by 3.71 % and peak electricity load by 38.62 % compared to the original strategy. Furthermore, it enhances the grid friendliness of the energy system by 42.64 %. The configuration of the battery energy storage system is also explored in conjunction with the optimization strategy to further improve grid friendliness. The impact of economic factors and thermal comfort on the configuration of the battery energy storage system is discussed. In the case study, raising the upper temperature limit by 1 °C can save at least 17.1 % in capacity, while battery energy storage system investment and operational costs can respectively be reduced by 55.04 % and 27.14 % within the thermal comfort range.

Calibration of a Hybrid Model for HVAC Systems for Fault Data Generation

The application of automated fault detection and diagnosis algorithms has proven to be an effective way to increase the energy efficiency of buildings. While data-driven strategies have demonstrated their potential in developing such algorithms, they require a set of historical data that represents both healthy and faulty equipment operation, which is not a common scenario in real facilities. This article presents a methodology for the generation of synthetic data on both healthy and faulty operation for building heating and air conditioning equipment, which is especially useful for supervision and failure detection in the case of replication and expansion of buildings in tertiary or residential building planning programmes. This work investigates the automatic calibration of a typical air-handling unit hybrid model, composed of the detailed parametric representation of the system components and its control sequence coupled with a simplified thermal load. The model is then extended with fault models that resemble commonly encountered component faults to generate operation data in faulty scenarios. The proposed framework is validated in a real use case with the calibration of an air-handling unit system and its control sequence using 15 days of data gathered from the building management system. The results obtained from the injection of seven typical faults are then compared with the fault-free simulations to highlight and validate their impact on key operating variables. The results prove the capabilities of this methodology to support database generation in the fields of fault detection and advanced maintenance of buildings’ air conditioning systems for building replicas.

Development of an FDD model for an existing building using transfer learning

Building heating, ventilation, and air conditioning (HVAC) systems are affected by several errors that can cause thermal discomfort to occupants and waste energy in buildings. Therefore, early and accurate Fault Detection and Diagnosis (FDD) is vital for enhancing the system’s efficiency. Data-driven FDD is promising because it is convenient compared to the first-principles-based rule set that demands in-depth expertise. However, to realize data-driven FDD for real-life cases, the data-imbalance problem in FDD must be solved. In this study, the authors suggest a novel approach that generates synthetic data from a building system simulation tool, HVACsim+, using them as a source model to apply transfer learning to a target air handling unit system. Even though from the existing target system only the normal operational data were available, the transfer learning approach was satisfactory, confirming that the proposed method effectively mitigates data imbalance in developing data-driven FDD.

The Cost-Optimal Control of Building Air Conditioner Loads Based on Machine Learning: A Case Study of an Office Building in Nanjing

Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.

Innovative silica aerogel fiberglass walls and roofs: A path to lower carbon emissions and higher energy savings

ABSTRACT Buildings account for a significant portion of global energy use and consumption. Buildings have substantial energy consumption due to the use of heating, ventilation, and cooling systems. It is evident that the insulation materials used in the design of building envelopes can efficiently reduce the cost of air conditioning by reducing external heat gains and losses. Additionally, the use of environmentally friendly and cost-effective can be beneficial from an environmental standpoint. This paper aims to evaluate the potential for cost savings in the air conditioning of buildings incorporated with silica aerogel fiberglass insulation in hollow concrete bricks (wall) and hollow roof tile (roof). Silica aerogel fiberglass insulation is extensively used for heat insulation purposes, including building insulations, due to its superior thermal insulation and flame retardant properties. The thermo-physical properties of silica fiberglass insulation and building materials were determined experimentally as per ASTM D 5334 standards. Twenty distinct building design concepts are investigated in this study, each featuring insulation-integrated concrete blocks composed of silica aerogel fiberglass: (WEM1) insulation layer in the outer side, (WEM2) insulation layer in the center, (WEM3) insulation layer in the inner side, (WEM4) insulation layers in the outer-middle, (WEM5) insulation layers in the middle-inner, and (WEM6) insulation layers in the outer-inner sides. Additionally, two arrangements of insulation-stuffed roof tiles, REM1 above the RCC and REM2 below the RCC. In addition, novel building designs integrated with insulation were analyzed numerically, related to environmental conditions that refer to two different scenarios in India (hot dry and composite climates). Thermo-economic analysis of novel wall and roof designs with silica fiberglass insulation materials on air-conditioning costs, carbon dioxide emissions, and payback time are compared with conventional roof and wall patterns. The thermo-economic analysis was carried out based on the number of degree-hours of heating and cooling to determine the building’s annual energy usage. The building model arrangement (WEM6-REM1), which consists of an insulation composite wall (WEM6) and insulation roof tile (REM1), demonstrated the greatest annual air conditioning cost savings of 3.4 $/m2/year and 1.74 $/m2/year when compared to conventional building design for climatic conditions of Kurnool and Gauhati. The building model arrangement (WEM6-REM1) composed of silica fiberglass aerogel insulation exhibits the highest reduction in carbon emissions of 65 kg/kWh and 33 kg/kWh, when compared to conventional building design analyzed in both hot-arid; (Kurnool) and composite (Gauhati) climatic conditions. The placement of insulation in the roof alone (REM1) has the shortest payback duration of 2.15 years. The results of this study point to a viable approach to enhancing building design decision-making and narrowing the performance gap in sustainable and energy-efficient strategies.

Experimental study on the performance of the vertical shell-tube indirect evaporative cooler with inner grooved tubes

The vertical tube indirect evaporative cooler (VTIEC) could be preferred one to air-conditioning buildings if its water film quality could be improved. This paper proposed a new VTIEC with inner grooved tubes to improve the water film quality and obtained its performance by experimental method. The results indicated that inner grooved tubes have positive effects on the water film and performance of the new VTIEC. A low working air enthalpy at inlet can significantly improve the total cooling capacity from evaporation, and then VTIEC performance. In this paper conditions, the velocity ratio of 1.52 of the produced air to the working air is better one condition compared to 1.01 and 2.28. The produced air velocity is positive effect on the cooling capacity per unit area until the produced air velocity of about 4.05 m/s and is negative effect on COP. The maximum unit cooling capacity, wet-bulb efficiency and COP are 307.74W/m2, 81.7% and 44.97.The results will promote the application of the new VTIEC.

Coordination Compound Guided a Novel Composite Film with Zn2V2O7 Nanoflake and VO2@Zn2V2O7 Nanoparticle for Enhanced Thermochromism Smart Window.

Thermochromic vanadium dioxide (VO2) can intelligently modulate the transmittance of indoor solar radiation to reduce the energy consumption of air conditioning in buildings. Nevertheless, it remains a great challenge to simultaneously improve the luminous transmittance (Tlum) and solar modulation ability (ΔTsol) of VO2. In this study, a novel approach is employed utilizing a coordination compound to finely tune the growth of a VO2 based composite film, yielding a hierarchical film comprising Zn2V2O7 nanoflakes and VO2@Zn2V2O7 core-shell nanoparticles. Remarkably, the resulting composite films showcase exceptional optical performance, achieving a Tlum of up to 73.0% and ΔTsol of 15.7%. These outcomes are attributed to the antireflection properties inherent in the nanoflake structure and the localized surface plasmon resonance of well-dispersed VO2 nanoparticles. In addition, the Zn2V2O7-VO2 film demonstrates remarkable environmental durability, retaining 90% of its initial ΔTsol even after undergoing aging at 100°C under 50% relative humidity for a substantial period of 30 days - a durability equivalent to ≈20 years under ambient conditions. This work not only achieves a harmonious balance between Tlum and ΔTsol but also introduces a promising avenue for the design of distinctive composite nanostructures.

Reflective coatings: Enhancing building performance and sustainability

Reflective coatings stand at the forefront of sustainable construction, offering a powerful solution to industry challenges posed by climate change, resource scarcity, and rising energy costs. These innovative coatings significantly enhance energy efficiency, improve thermal comfort, and contribute to urban heat management by reducing buildings' absorption of solar heat. This leads to a decreased reliance on air-conditioning systems, resulting in substantial energy savings and a lower carbon footprint for building operations. Our comprehensive study examines these coatings' application on roofs, walls, and windows, confirming their effectiveness in maintaining cooler building interiors and reducing the demand on mechanical cooling systems. By meticulously analyzing materials, application techniques, and modeling methodologies, the paper elucidates the coatings' pivotal role in promoting environmentally responsible building practices. The findings reveal that reflective coatings have the potential to reduce solar heat gain by about 40 %. This reduction can lead to a corresponding indoor temperature drop of 2–4°C in naturally ventilated buildings or a decrease in cooling energy use in air-conditioned buildings, provided the air conditioning system is not undersized. These energy savings are particularly significant in urban environments, where widespread adoption could potentially lower ambient temperatures by approximately 1°C, thus addressing the urban heat island (UHI) effect more effectively. In conclusion, our research offers a quantified assessment of reflective coatings, emphasizing their value within the broader discourse of environmentally responsible construction. Their substantial potential to support urban sustainability objectives is evident, marking them as a promising avenue for future development and implementation.

Assessment of energy saving and CO2 emission mitigation of low-lift cooling in computer hardware manufacturing industry: A case study in Thailand

Abstract Released heat from production process contributes the largest proportion of cooling load in computer hardware manufacturing industry. According to traditional design practice, chiller plant produces chilled water at a temperature of 7°C to treat the entire load from the production process and from the comfort air-conditioning in buildings. However, the heat from the process is solely the sensible component where high-temperature chilled water (e.g., 15°C) can be used for the heat removal, the chillers equipped with low temperature-lift technology thus offer a great opportunity to improve the plant’s cooling performance. Under the critical global warming situation and the need of decarbonizing industry, this paper investigated the energy saving and CO2 emission mitigation potential of the low-lift technology by which the chiller plant of high-temperature chilled water (15°C) is dedicated for the production process cooling, and the traditional chiller plant of the low-temperature chilled water is used to serve the building air-conditioning. The study site was a large factory of computer hardware manufacturing in Thailand. Through detailed energy auditing, the cooling load profile of the production process was established, and it was used to design the low-lift chiller plant. The annual simulation results showed that in our study case the low-lift chiller plant together with variable motor speed control had 31.6% higher cooling performance than the traditional chiller plant. The plant could reduce the energy consumption by 5,550 MWhelec/year. As the CO2 emission of the grid power in Thailand was 0.47 kgCO2/kWh in 2023 and it was expected to reduce to 0.35 kgCO2/kWh in 2050, the CO2 emissions from implementing the low-lift cooling could be reduced by 49,400 TonCO2 over a course of the 20-year project lifetime. The study results encouraged the low-lift cooling technology as a practical energy efficiency measure in computer hardware manufacturing industry. To our knowledge, while past research focused on the low-lift cooling in building air-conditioning, the study was the first potential assessment project of the low-lift cooling as a deep decarbonization technology for the computer hardware manufacturing industry in Thailand.

Performance analysis of ground source heat pump systems for constant temperature and humidity air conditioning in industrial buildings: a case study

Abstract The ground source heat pump (GSHP) system has gained widespread popularity for its provision of efficient and environmentally sustainable cooling and heating solutions for buildings. While the performance of GSHP systems has raised significant concerns, research predominantly focuses on residential and office buildings, leaving a gap in understanding their applicability in industrial settings with constant temperature and humidity air conditioning needs. This study aims to address this gap by evaluating the performance of a hybrid ground source heat pump (HGSHP) system implemented within a cigarette factory located in the hot summer and cold winter (HSCW) of China. Through a comprehensive analysis of operational data and long-term monitoring under three cooling conditions, essential parameters including water temperature characteristics, indoor temperature and humidity, system efficiency, power consumption, and soil temperature distribution were examined. The findings revealed that the weighted average coefficient of performance (COP) of the heat pumps (COPhp) and the whole system (COPsys) was decreased from 5.05 to 4.32 and 3.32 to 2.89, respectively. A positive correlation was observed between low cooling load high energy consumption, and low COP. The inlet temperature of the condenser exhibited a declining trend, which was attributed to the intermittent operation of condensation heat recovery. Furthermore, a comprehensive analysis of indoor temperature and humidity distribution within the controlled room was presented and found that almost all the rooms serviced by this system meet the specified design requirements. This research contributes to an enhanced understanding of the viability of HGSHP systems in industrial settings and provides valuable references for improving their energy efficiency and overall performance.

Automatic control of constant temperature and humidity in building air conditioning systems based on frequency domain analysis

How to solve their automatic control of constant temperature and humidity gradually becomes a research hotspot as the continuous upgrading of air conditioning systems. This study aims to optimize the traditional proportional-integral-differential controller for improvement to solve the time-delay instability phenomenon in temperature and humidity control. The objective of this study is to optimize existing proportional-integral-differential controllers to improve the time-delay instability problem that is common in temperature and humidity control. Firstly, it treats the controlled object as a first-order and second-order system with time-delay characteristics. Next, the Smith predictor controller is generalized equivalent to ensure that the equivalent system does not contain time-delay. Finally, an analysis of the first-order and second-order closed-loop control system is conducted by combining Smith predictive controller and proportional-integral-differential controller. The system achieves the goal of automatic control of constant temperature and humidity by adjusting the control parameters. The experiment showcased that the temperature control time of the proposed control scheme under first-order and second-order time-delays was 16 s and 3 s, respectively. Meanwhile, the humidity control time was 14 s and 13 s, respectively. In practical applications, the proposed control scheme achieved good control effects in all four seasons. This indicates that the controller designed in this study possesses good control performance. It also can achieve the goal of constant temperature and humidity control. This can provide technical support for the automation control of air conditioning systems.

Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty

The rising demands for comfort alongside energy conservation underscore the importance of intelligent air conditioning control systems. Model Predictive Control (MPC) stands out as an advanced control strategy capable of addressing these demands. However, accurate prediction of all relevant variables remains a challenge in practical scenarios, complicating MPC’s ability to devise effective control actions amid prediction inaccuracies. To counteract this issue, this paper introduces an enhanced Double-Layer Model Predictive Control (DLMPC) algorithm. This innovative approach adjusts for discrepancies between forecasted and actual values without the need for additional variables and models, thereby reducing the adverse effects of prediction errors. Additionally, we develop precise models for room temperature simulation and for calculating air conditioning (AC) load and energy consumption, grounded in empirical data from residential settings and AC performance tests. Validation of these models demonstrates their efficacy in enabling MPC to formulate efficacious control strategies. When juxtaposed with a baseline model, the DLMPC algorithm significantly improves temperature regulation accuracy by up to 15.12% and achieves a 10.50% reduction in energy consumption over the heating season.

Water quality constraints H2O2 production in a dual-fiber photocatalytic reactor

In-situ hydrogen peroxide (H2O2) finds applications in disinfection and oxidation processes. Photoproduction of H2O2 from water and oxygen, avoids reliance upon organic chemicals, and potentially enables smaller-sized or lower-cost reactors than electrochemical methods. In ultrapure water, we previously demonstrated a novel dual-fiber system coupling a light emitting diode (LED) with a metal-organic framework (MOF) catalyst-coated optical fiber (POF-MIL-101(Fe)) and O2-based hollow-membrane fibers and achieved a remarkable H2O2 yield, 308 ± 1.4 mM h−1 catalyst-g−1. To enable H2O2 production anywhere we sought to understand the impacts of common water quality parameters. The production of H2O2 was not affected by added sodium, potassium, hydroxide, sulfate or nitrate ions. There was consistent performance over a wide pH range (4–10), maintaining a high production rate of 232 ± 3.5 mM h−1 catalyst-g−1 even at pH 10, a condition typically unfavorable for H2O2 photoproduction. Chloride ions produced hypochlorous acid, consuming in-situ produced H2O2. Phosphate adsorption on the iron-based MOF catalysts blocked H2O2 production. Inorganic carbon species inhibited H2O2 production due to in-situ formic acid. Encouraging results were obtained using atmospheric water (i.e., condensate), with rates reaching 288 ± 6.1 mM h−1 catalyst-g−1, comparable to ultrapure water. This underscores atmospheric water as a variable alternative, available in nearly all building air conditioning systems or could overcome geographical constraints, particularly in regions where obtaining pure water resources is challenging, offering a cost-effective solution. The dual-fiber reactor using atmospheric water enables high-efficiency H2O2 production anytime and anywhere.

Applying OPTICS with and without PCA for fault detection of fan coil units using building automation system data

In building operations, Heating, Ventilation, and Air Conditioning (HVAC) system faults lead to substantial energy waste. Due to the rapid growth in the availability of sensing data and advancements in computational technology, AI-based methods have emerged as a powerful tool for Fault Detection and Diagnosis (FDD) of HVAC systems, significantly enhancing the efficiency of building operations. Several studies support supervised learning methods for FDD; however, these are limited in practice due to their reliance on labeled datasets, which are generally unavailable. In this context, unsupervised methods, notably those using cluster analysis, show great promise, particularly for analysing Building Automation System (BAS) data. This paper presents a methodology using OPTICS – an ordered clustering algorithm – for fault detection and explores the impact of PCA on its accuracy for fault detection, using both published and full-scale historical building data. While beneficial for reducing computational costs and enhancing noise reduction — thereby generally improving the clarity of cluster differentiation — PCA’s dimensionality reduction may result in the loss of critical information, leading to some clusters being less discernible or entirely undetected. These overlooked clusters could be indicative of underlying faults, and their obscurity represents a significant limitation of PCA when identifying potential faults in complex data.

Offset-Free Koopman Model Predictive Control of Thermal Comfort Regulation for a Variable Refrigerant Flow-Dedicated Outdoor Air System-Combined System

Abstract Variable refrigerant flow (VRF) system has been an appealing solution of air conditioning for residential and commercial buildings, due to its flexibility and cost effectiveness, while lack of ventilation capability is a major drawback. Incorporation of dedicated outdoor air system (DOAS) is a typical practice. However, good coordination between DOAS and VRF is critical for achieving desired thermal comfort is challenging due to the possible complexity of mixed sensible and latent heat exchanges. In this paper, to handle the nonlinear dynamic characteristics of VRF-DOAS system, we propose an offset-free Koopman model predictive control (MPC) strategy for thermal comfort regulation, in which the MPC design is computationally more efficient due to the convex problem formulation and the use of reduced-order Koopman models, and the offset-free MPC structure enhances the robustness to model uncertainties and unmeasured disturbances. A control-oriented model is obtained by hybridizing the first-principle and data-driven modeling approach. The proposed controls strategy is evaluated with a Modelica simulation model of a VRF-DOAS system. A Dymola-Python cosimulation platform is developed via the functional mockup interface (FMI), for which the MPC algorithms are implemented in Python. Simulation results show significantly better performance of the offset-free Koopman MPC in thermal comfort regulation.

Performance Evaluation of Water Cooled Condenser of Air Conditioner Working on VCRS

VCRS commonly known as (Vapour Compression Refrigeration System) is widely adopted method for air conditioning of buildings, in pharmaceutical industry for storage of medicine, in hospitals, automobiles etc. There are several methods for increasing the efficiency% (COP) of an VCRS cycle. Some of the modification can be done in Compressor, Condenser, Evaporator, and in Refrigerant . In this experiment, we adopted method of Subcooling to reduce Compressor work. This study investigates the implementation of subcooling in the condenser of a Vapour Compression Refrigeration System (VCRS) to enhance its efficiency and reduce power consumption. The traditional condenser design is modified by submerging it in a water tank, allowing for subcooling of the refrigerant. The subcooling process involves lowering the temperature of the refrigerant below its saturation temperature, which improves the system’s Coefficient of Performance (COP) by increasing the density of the refrigerant entering the expansion valve. This modification aims to optimize the heat rejection process and enhance the overall performance of the refrigeration system .Experimental result demonstrate a significant reduction in power consumption and an improvement in COP, highlighting the effectiveness of the condenser subcooling method in enhancing the efficiency of VCRS. Keywords : Compressor, Condenser, Expansion Valve, Evaporator.

Building heating load forecasting based on the theory of transient heat transfer and deep learning

Reliable and accurate heating load forecasting can provide comprehensive information for the monitoring and control of Heating, Ventilation, and Air Conditioning (HVAC) in buildings, which reduce uncertainty on the load demand of energy effectively. Data-driven method has demonstrated a potential to forecast heating load, but current methods generally lack the consideration of features on model forecasting performance and applicability. Based on the theory of transient heat transfer and conduction transfer function for building hourly heating load calculation, this study proposes an idea that comprehensively selects physical variables affecting the hourly dynamic changes of the building heating load as features, which enhances the persuasiveness of selecting datasets features. Then we use the deep learning method to build short-term heating load forecasting models, and explore the influence of different feature combinations and time steps on the model forecasting performance. Combining the physical property of building heat gains, we divide the features into external heat gains, internal heat gains and historical heating load and then show their performance of models at different time steps. It is shown that the two-hour data of external heat gains and internal heat gains, the three-hour data of past heat load have the highest value. The best model can achieve a mean absolute error of 0.153 kW and a mean absolute percentage error of 6.2 %. These findings are helpful for researchers to select more relevant features to forecast heating load in buildings and improve their models through physical aspect.

Evaluation of Energy Density in Hexadecane Phase Change Emulsions in Comparison to Water

The development of energy-dense, thermomechanically stable, and low-viscous phase change emulsions (PCMEs) is proposed as an alternative thermal energy storage solution for building air conditioning. A set of oil-in-water (O/W) nanoemulsions with hexadecane concentration varied between 10, 20, 25, 30, 35, and 40 wt. % is prepared and characterized with respect to their physical, thermal, and rheological properties. The storage characteristics are evaluated in terms of storage density, phase transition behaviour, supercooling, and dynamic viscosity. A systematic comparison in terms of energy density between the PCMEs and water is carried out at different temperature conditions. For this purpose, the storage break-even temperature TBE is proposed as a novel parameter to determine suitable operating temperature ranges and cycling conditions. The cycle stability is evaluated by rheological measurements, applying thermomechanical loads to the samples for a high number of cycles. According to the results, the energy density of the PCMEs is always higher than that of water, when the minimum temperature used for the cycling is below the storage break-even temperature. The emulsion with 30 wt. % hexadecane fraction is considered particularly promising, thanks to its high stability when exposed to thermomechanical stress, relatively low viscosity between 10 and 22 mPa s (0–30°C), and a storage density of 98 MJ/m3 within a cycling temperature range of 12 K.

Unveiling overlooked aspects of model predictive control for building air conditioning systems

The ongoing research on model predictive control (MPC) for building air conditioning systems predominantly centers on improving the predictive capabilities of system models. In this paper, the impacts of three additional pivotal factors on MPC performance are assessed by examining a generic MPC design for a typical variable air volume (VAV) system that serves large commercial buildings. The three factors encompass the nuanced reformulation of optimization, the judicious relaxation of constraints, and the meticulous tuning of parameters. Detailed case studies with an integrated Modelica and EnergyPlus model of the US Department of Energy's Commercial Reference Building are conducted. The results confirm that the optimization formulation, along with relaxation methods, significantly affects MPC performance in terms of energy savings, zonal thermal comfort level, and computational demand. They also reveal that the impact of the MPC control parameters on the energy savings and thermal comfort may vary by season and can be non-monotonic.