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• Models of the impact of indoor thermal environment on MDPC thermal comfort were established. • The effect of activity levels on thermal comfort in MDPC was quantitatively analyzed. • The relationship among temperature and humidity in different parts and working time and intensity was established. • The acceptable regions inside MDPC were proposed by the enthalpy-humidity chart. With the outbreak of infectious diseases such as Corona Virus Disease 2019, medical staff work intensively in isolated plots, medical disposable protective clothing (MDPC) has poor air condition and humidity permeability, which seriously reduces the thermal comfort of medical staff. In this paper, the effect of indoor thermal environment and activity levels on thermal comfort inside MDPC was studied by experiment. Five parts of the body were measured inside MDPC and the appropriate movements were chosen to simulate different levels of labor intensity. Meanwhile, physiological parameters and subjective thermal sensation were statistically analyzed. The results showed the influence range of different indoor temperatures on the temperature and humidity inside MDPC was about 1 °C and 10 %, respectively; it indicated that the environment inside MDPC could be improved by reducing indoor temperature, that is, a cross intelligent adjustment mode was proposed. The effect of labor intensity on the temperature inside MDPC was significantly less than that of humidity. Within 20 min, the humidity changes under moderate and heavy labor intensity were even more than 10 %, and the subjective discomfort threshold of the subjects increased by nearly 50 %. Furthermore, the maximum benefit could be obtained by concentrating cooling on back, forehead, chest and upper arm. Theoretical models of working time, labor intensity, and temperature and humidity inside MDPC under different indoor temperatures and different parts were given. In addition, acceptable regions inside MDPC which were approximately parallelogram in the enthalpy-humidity chart. These conclusions could be a reference for future thermal comfort inside MDPC research. [ABSTRACT FROM AUTHOR]

The vehicle HVAC (heating, ventilating, and air conditioning) system aims to keep passengers thermal comfort and reduce energy consumption. One effective measure is to use temperature prediction to optimize HVAC systems. This paper aimed to comprehensively investigate the potential of machine learning methods applied to cockpit temperature prediction and proposed a deep learning method considering the Spatio-temporal correlation of the temperature field. Specifically, this study constructs a topological description of the temperature field inspired by the spatial and temporal correlations revealed by the Navier-Stokes equations. The Graph Convolutional Network (GCN) is used to capture the topological structure to obtain spatial features, and the Gated Recurrent Unit (GRU) is used to capture the dynamic change of node attribute to obtain temporal features. Finally, the GCGRU model can extract Spatio-temporal features of the temperature field. The experimental results show that the prediction method using Spatio-temporal features for the temperature field is feasible. The prediction performance is better than all the baseline methods and has the robustness to the data noise. This work is enlightening and may have a further reference to the feasibility study of the vehicle cabin air temperature prediction model. [ABSTRACT FROM AUTHOR]

[Display omitted] Pre-cooling is a method to shift peak air conditioning demand to off-peak and shoulder hours by utilizing energy storage in the thermal mass of buildings. This paper evaluates the pre-cooling potential of a range of residential buildings broadly representative of the Australian housing stock. Pre-cooling in 2-, 6-, and 8-star buildings with light, medium, and heavy construction weights in the cities of Adelaide, Brisbane, Melbourne, and Sydney is simulated, and its impacts on household bills, thermal comfort and peak air-conditioning demand reduction are assessed. Star rating is the method for rating Australian buildings based on their annual heating and cooling demand. The housing assessment by both star rating and construction weight highlights the varied potential of pre-cooling. Pre-cooling sensitivity to air conditioning size is also evaluated by simulating 5 k W th , 15 k W th , and 25 k W th air conditioning sizes representing typical small, medium, and large units in the Australian air conditioning market. Using an adaptive thermal comfort model, the impact of people's thermal comfort expectations on pre-cooling is also considered. The results highlight that compared with 2-star buildings, the percentage of daily cost savings from pre-cooling is considerably higher in 6-star and 8-star buildings. Moreover, with a small air conditioner, 2-star buildings are prone to negative seasonal cost savings as the air conditioner is not able to achieve thermostat temperature settings and hence runs longer. However, pre-cooling does offer considerable discomfort reduction to household members. A maximum summer seasonal cost savings of around $260 is observed in a 6-star light-weight building in Adelaide, and a 2-star light-weight building in Brisbane. Households that accept wider temperature variations with pre-cooling may not necessarily see cost savings. These households do provide greater peak air conditioning demand reductions. In conclusion, the value of pre-cooling for households and network peak demand reduction depends significantly on the housing type and location but can be highly valuable in many cases. [ABSTRACT FROM AUTHOR]

• Implementation of a model predictive control scheme for HVAC systems is presented. • The hardware/software solution is generalisable to most building management systems. • Cloud solutions can offer low-cost, scalable control in commercial buildings. • Economic MPC reduced energy usage by 1.7% compared to baseline control. • Building thermal performance strongly affects viability of MPC schemes. Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation of the building's thermal behaviour. When simulated across two months the MPC approach performed better and achieved the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (an improvement of 1.7% compared to the baseline), while the carbon optimisation gave negligible CO 2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies, however the level of impact will depend on specific building attributes. [ABSTRACT FROM AUTHOR]

Faults in heating, ventilation and air conditioning (HVAC) systems cause increased energy consumption, degrading thermal comforts, growing operational cost and reduced system lifespan. An effective evaluation of fault effects is critical to the development of various fault diagnostics solutions, the improvement of operation maintenance and the optimization of monitoring systems. In the HVAC area, a majority of research work in evaluating fault effects was to analyze energy consumption impacts or thermal comfort impacts. However, a handful of research has been conducted on evaluating fault effects on various measurements, which are increasingly employed to monitor equipment's operation. Fault effects on various measurements may display different symptom patterns and present changed sensitivities when the equipment operates under various faults, severity levels, as well as operation conditions. However, a long-term observation of fault symptoms under various operation conditions, different fault types and severity levels to evaluate fault effects is extremely challenging. In this paper, a simulation-based framework was proposed to evaluate fault effects in fan coil units (FCUs). Two metrics namely fault symptom occurrence probability (SOP) and fault symptom daily continuous duration (SDCD) were developed to quantify fault symptoms under various FCU faults. A total of 18 common FCU faults at different severity levels were implemented on the developed HVACSIM+ simulation platform to obtain a full year fault inclusive data set for 48 fault simulation cases. The framework, as well as obtained SOP and SDCD distributions will benefit multiple folds such as the development of probability-based fault diagnostics inference approaches, optimization of sensor location, and fault prioritization. [ABSTRACT FROM AUTHOR]

• Heating and cooling setpoint temperature of thermostats were optimized in a residential building for different climates of Iran. • GMDH-type neural network and NSGA-II algorithm were used for multi-optimization. • XPS is the optimum insulation for all the climates in Iran. • PPD reduces with thickness of insulation for all the cities, except Bandar Abbas. Determination of the optimum setpoint temperature of thermostats in various climates is a problem in air conditioning of residential buildings. In this paper, a new method is developed to optimize the setpoint temperature of thermostats in different climates of Iran. Design variables in the optimization process are heating setpoint, cooling setpoint, thickness, and thermal conductivity of insulations in the building envelopes. The optimization goals are minimizing energy consumption and cost of insulations in addition to maximizing thermal comfort of occupants. Thus, the static payback period (SPP) and the predicted percentage dissatisfied (PPD) indices are selected as objective functions which should be minimized in the optimization process. The methods applied to attain these objectives are numerical modeling by EnergyPlus software, Grouped Method of Data Handling (GMDH) type of Artificial Neural Network (ANN), and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Therefore in this process, first, EnergyPlus is used to train the neural network. Afterward, the GMDH-type neural network is applied to derive polynomials computing the objective functions from the design variables. Then, Pareto optimal points for the objective functions are obtained through using these polynomials and NSGA-II multi-objective optimization. Finally, the optimum design point is selected for different cities. According to the results, type and thickness of insulation integrated in the building envelopes affect the static payback period and thermal comfort of occupants. For all the climates of Iran, the most appropriate insulation is XPS and the optimum heating setpoint of thermostat is 22 °C. Also, the optimum value for the cooling setpoint pertains to the type of climate, so that this value for Bandar Abbas, Yazd, Tehran, Rasht and, Tabriz is, respectively, equal to 24.5, 24.7, 25.2, 25.3, and 25.6 °C. Moreover, thermal comfort of occupants increases with thickness of insulation, except for Bandar Abbas whose PPD is almost constant. The most value of PPD reduction with insulation thickness is related to Tehran where by increasing the insulation thickness from 1 cm to 5 cm, PPD decreases up to 53%. [ABSTRACT FROM AUTHOR]

• The potential of energy saving by using adaptive setpoint temperatures. • The influence of thermostats according to their accuracy: 0.1, 0.5, and 1 °C. • The analysis of two case studies in the current scenarios, 2050 and 2100. • The 0.5 and 1 °C thermostats achieved energy savings of more than 40%. Building energy performance should be improved to reduce the impact of climate change. The energy saving potential has been recently proved with adaptive setpoint temperatures. However, the accuracy of thermostats hinders the achievement of the energy saving obtained in previous studies. For this reason, this paper studies the influence of three types of thermostats according to their configuration accuracy: 0.1, 0.5, and 1 °C. Two case studies (with and without retrofitting) were analysed in three cities in the current scenario, in 2050, and in 2100. The results showed that the implementation of adaptive setpoint temperatures in thermostats of 0.1 °C virtually obtains the same savings as the direct application of thermal comfort limits. Nevertheless, obtaining considerable energy savings in the other two thermostats depends on the type of energy consumption, climate, and the category of the thermal comfort model. The application of adaptive setpoint temperatures in air conditioning systems obtains energy savings greater than 40%, regardless of the type of thermostat and category, whereas in heating systems, only the category III obtains energy savings with old thermostats. [ABSTRACT FROM AUTHOR]