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Before the COVID-19 pandemic, ventilation in buildings was not always given its due importance. The World Health Organization has highlighted the important role of air exchange with the outdoors in improving the air quality in buildings; buildings should, therefore, be equipped with mechanical ventilation or adequate air conditioning systems. This paper aims to investigate different retrofit solutions for air conditioning, evaluating them in terms of energy consumption and cost and the impact of increased outdoor air exchange rates on countering the propagation of COVID-19; the latter is the main novelty of the paper. As a case study, we take an existing office building located in Central Italy that was previously not equipped with a mechanical ventilation system (a system with primary air was introduced during the study). The energy analysis was conducted using dynamic simulation software after validation through energy bills; energy and economic analyses were conducted considering different external-air exchange rates. An optimal number of outdoor air changes was found to mitigate the risk of COVID-19 infection, a finding in line with the international literature. The increase in air changes with outdoor air leads to a rise in energy consumption and costs. These values were evaluated for different air conditioning systems and operational schedules. These drawbacks can be made less significant by combining interventions in the system with energy-efficiency measures applied to the building envelope. [ABSTRACT FROM AUTHOR]

The paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experimentally validated e-bus simulation model, and it consists of powertrain and heating, ventilation, and air conditioning (HVAC) system submodels. The main advantage of the proposed approach is its reliance on readily available trip-related data, such as travel distance, mean velocity, average passenger count, mean and standard deviation of road slope, and mean ambient temperature and solar irradiance, as opposed to the physical model, which requires high-sampling-rate driving cycle data. Additionally, the data-driven model is executed significantly faster than the physical model, thus making it suitable for large-scale city bus electrification planning or online energy consumption prediction applications. The data-driven model development began with applying feature selection techniques to identify the most relevant set of model inputs. Machine learning methods were then employed to achieve a model that effectively balances accuracy, simplicity, and interpretability. The validation results of the final eight-input quadratic-form e-bus model demonstrated its high precision and generalization, which was reflected in the R2 value of 0.981 when tested on unseen data. Owing to the trip-based, mean-value formulation, the model executed six orders of magnitude faster than the physical model. [ABSTRACT FROM AUTHOR]

In a power system with a high proportion of renewable energy, sudden increases in wind power or photovoltaic output can lead to huge challenges, such as difficulties in accommodating excess renewable energy and imbalances between supply and demand on the grid. As an important adjustable resource on the demand side, air conditioning load is a flexible load for realizing output consumption. In this paper, a heterogeneous air conditioning load regulation strategy for renewable energy consumption is proposed. Each air conditioning load regulation quantity is obtained based on the day-ahead dispatching mode. Then, the temperature setting value, rated power, and duty cycle are selected as the indexes. The load regulation sequence is obtained by the entropy weight method. Finally, the load regulation time of each air conditioning load is obtained based on the constraint of the quantity of loads during the possible adjustment time. The simulation analysis shows that the temperature regulation strategy presented in this paper can effectively reduce the power fluctuations of air conditioning loads, while ensuring that users with lower temperature settings are selected in the adjustment process. [ABSTRACT FROM AUTHOR]

The humidifier plays a key role in a humidification–dehumidification (HDH) desalination system; it directly affects both the freshwater production efficiency and energy consumption ratio of the system. In this study, for a humidifier in an HDH system, a heat–mass coupled differential equation model of spray water and air on the surface of the packing material was established, and the effects of parameters such as the spray water temperature (tw), mass flow rate of spray water (mw), air temperature (ta), and air mass flow rate (ma) on the humidification performance of humidifiers composed of eight different types of packing materials were investigated. The results show the following: (1) Under the same inlet spray water and air conditions, the humidification performance of different packing materials from good to bad is as follows: cellulose paper, polypropylene, hackettes, saddles, snowflakes, wooden slats, polyvinyl chloride, gunny bag cloth. (2) Increasing the tw can significantly improve the humidification performance. To achieve higher humidification energy efficiency, it is recommended to increase the tw to above 80 °C. (3) With the increase in the mw, although the humidification efficiency (εhum) decreases slightly, the humidification rate (mhum) increases, and the specific humidification energy ratio (ηhum) decreases accordingly. To maintain a high mhum and a low ηhum, it is advisable to control the mw at not less than 0.5 kg/s. (4) Increasing the humidifier inlet ta can improve the mhum, εhum, and ηhum, although not as effectively as increasing tw. (5) Increasing the ma can improve mhum and εhum. However, it simultaneously increases the ηhum. The results of this study can provide theoretical guidance for the selection of efficient packing materials and the optimization of humidifier operating conditions in HDH desalination systems. [ABSTRACT FROM AUTHOR]

A chilled water system transports cooling functionality from refrigerators to users via heating, ventilation, and air conditioning (HVAC) systems. This paper investigated an optimal control strategy to regulate the volume flow rate of each user branch in a chilled water system, considering the minimum resistance operation to reduce energy consumption. An artificial neural network (ANN) was adopted to establish the nonlinear relationship between the volume flow rate of each user branch, pump frequency, and valve opening of each user branch. An optimal control strategy for a chilled water HVAC system is proposed in this article, according to the pump-valve combined control (PVCC) principle and an ANN model, i.e., pump-valve combined control using an artificial neural network model (PVCC-ANN). A series of tests were conducted to collect data to train the ANN model and analyze the performance of the PVCC-ANN in an experimental chilled water system. The results show that the trained ANN model has good prediction performance. A minimum resistance operation can be achieved to control the volume flow rate of each user branch independently by using the PVCC-ANN model. Moreover, the proposed PVCC-ANN method shows good energy-saving performance in chilled water systems, which can be attributed to the minimum resistance operation. Taking the energy consumption of the pump's constant frequency operation as the reference, the energy saving rate using the PVCC-ANN is between 14.3% and 58.6% under 10 operating conditions, as reported in this paper. [ABSTRACT FROM AUTHOR]

The HVAC (Heating, Ventilation, and Air Conditioning) system is an important component of a building's energy consumption, and its primary function is to provide a comfortable thermal environment for occupants. Accurate prediction of occupant thermal comfort is essential for improving building energy utilization as well as health and work efficiency. Therefore, the development of accurate thermal comfort prediction models is of great value. Deep learning based on data-driven techniques has excellent potential for predicting thermal comfort due to the development of artificial intelligence. However, the inability to obtain large quantities of detailed thermal comfort labeling data from residents presents a substantial challenge to the modeling endeavor. This paper proposes a building-to-building transfer learning framework to make deep learning models applicable in data-limited interior building environments, thereby resolving the issue and enhancing model predictive performance. The transfer learning method (TL) is applied to a novel technology dubbed the Transformer model, which has demonstrated outstanding performance in data trend prediction. The model exploits the spatiotemporal relationship of data regarding thermal comfort. Experiments are conducted using the source dataset (Scales project dataset and ASHRAE RP-884 dataset) and the target dataset (Medium US office dataset), and the results show that the proposed TL-Transformer achieves 62.6% accuracy, 57% precision, and a 59% F1 score, and the prediction performance is better than other existing methods. The model is useful for predicting indoor thermal comfort in buildings with limited data, and its validity is verified by experimental results. [ABSTRACT FROM AUTHOR]

The building and construction sector has a significant impact on the CO2 emissions and pollutants released into the atmosphere, which contribute to climate change. The EPDB Directive mandates the achievement of minimum energy class E for all residential buildings by 2030 and energy class D by 2033. Particularly, in Italy, about 86% of the existing building stock predates the enactment of any energy laws or regulations, making it imperative to apply the energy efficiency interventions. This paper provides a support decision tool for the identification of the standardized interventions in the building envelope, the air conditioning system, and domestic hot water production. This study is focused on a specific construction period class (1976–1990) in six different climatic zones. The methodological approach is based on a cataloguing phase and the definition of ante operam energy classes as well as on case study identification, energy requalification intervention identification, solution simulations, and cost estimation. By simulating the standardized interventions for each climatic zone, a range of possible combinations is identified. The most advantageous ones are determined based on a cost–benefit analysis considering the potential class jump achieved. The research result is a matrix of energy efficiency interventions that is applicable to each climatic zone and can be extended to the existing housing stock. [ABSTRACT FROM AUTHOR]

This paper aims to study the workflow of the detection centre of stealthy attacks on industrial installations that generate an increase in energy consumption. Such long-lasting, undetected attacks on industrial facilities make production more expensive and less competitive or damage the installation in the long term. We present the concept of the remote detection system of cyberattacks directed at maliciously changing the controlled variable in an industrial process air conditioning system. The monitored signals are gathered at the PLC-controlled installation and sent to the remote detection system, where the discrepancies of signals are analysed based on the Control Performance Assessment indices. The results of performed tests prove the legitimacy of the adopted approach. [ABSTRACT FROM AUTHOR]

Accurate prediction of air-conditioning energy consumption in buildings is of great help in reducing building energy consumption. Nowadays, most research efforts on predictive models are based on large samples, while short-term prediction with one-month or less-than-one-month training sets receives less attention due to data uncertainty and unavailability for application in practice. This paper takes a government office building in Ningbo as a case study. The hourly HVAC system energy consumption is obtained through the Ningbo Building Energy Consumption Monitoring Platform, and the meteorological data are obtained from the meteorological station of Ningbo city. This study utilizes a Gaussian process regression with the help of a 12 × 12 grid search and prediction processing to predict short-term hourly building HVAC system energy consumption by using meteorological variables and short-term building HVAC energy consumption data. The accuracy R2 of the optimal Gaussian process regression model obtained is 0.9917 and 0.9863, and the CV-RMSE is 0.1035 and 0.1278, respectively, for model testing and short-term HVAC system energy consumption prediction. For short-term HVAC system energy consumption, the NMBE is 0.0575, which is more accurate than the standard of ASHRAE, indicating that it can be applied in practical energy predictions. [ABSTRACT FROM AUTHOR]

The present paper is focused on short-term prediction of air-conditioning (AC) load of residential buildings using the data obtained from a conventional smart meter. The AC load, at each time step, is separated from smart meter's aggregate consumption through energy disaggregation methodology. The obtained air-conditioning load and the corresponding historical weather data are then employed as input features for the prediction procedure. In the prediction step, different machine learning algorithms, including Artificial Neural Networks, Support Vector Machines, and Random Forests, are used in order to conduct hour-ahead and day-ahead predictions. The predictions obtained using Random Forests have been demonstrated to be the most accurate ones leading to hour-ahead and day-ahead prediction with R2 scores of 87.3% and 83.2%, respectively. The main advantage of the present methodology is separating the AC consumption from the consumptions of other residential appliances, which can then be predicted employing short-term weather forecasts. The other devices' consumptions are largely dependent upon the occupant's behaviour and are thus more difficult to predict. Therefore, the harsh alterations in the consumption of AC equipment, due to variations in the weather conditions, can be predicted with a higher accuracy; which in turn enhances the overall load prediction accuracy. [ABSTRACT FROM AUTHOR]

This paper aimed to evaluate the applicability of adiabatic humidification in the heating, ventilation, and air conditioning (HVAC) systems of semiconductor cleanrooms. Accurate temperature and humidity control are essential in semiconductor cleanrooms and high energy consumption steam humidification is commonly used. Therefore, we propose an adiabatic humidification system employing a pressurized water atomizer to reduce the energy consumption. The annual energy consumption of three different HVAC systems were analyzed to evaluate the applicability of adiabatic humidification. The studied cases were as follows: (1) CASE 1: a make-up air unit (MAU) with a steam humidifier, a dry cooling coil (DCC), and a fan filter unit (FFU); (2) CASE 2: a MAU with the pressurized water atomizer, a DCC, and a FFU; and (3) CASE 3: a MAU, a DCC, and a FFU, and the pressurized water atomizer installed in the return duct. The energy saving potential of adiabatic humidification over steam humidification has been proved, with savings of 8% and 23% in CASE 2 and CASE 3 compared to CASE 1, respectively. Furthermore, the pressurized water atomizer installed in the return duct exhibits greater energy saving effect than when installed in the MAU. [ABSTRACT FROM AUTHOR]

The small medium large system (SMLsystem) is a house built at the Universidad CEU Cardenal Herrera (CEU-UCH) for participation in the Solar Decathlon 2013 competition. Several technologies have been integrated to reduce power consumption. One of these is a forecasting system based on artificial neural networks (ANNs), which is able to predict indoor temperature in the near future using captured data by a complex monitoring system as the input. A study of the impact on forecasting performance of different covariate combinations is presented in this paper. Additionally, a comparison of ANNs with the standard statistical forecasting methods is shown. The research in this paper has been focused on forecasting the indoor temperature of a house, as it is directly related to HVAC-heating, ventilation and air conditioning-system consumption. HVAC systems at the SMLsystem house represent 53:89% of the overall power consumption. The energy used to maintain temperature was measured to be 30%-38:9% of the energy needed to lower it. Hence, these forecasting measures allow the house to adapt itself to future temperature conditions by using home automation in an energy-efficient manner. Experimental results show a high forecasting accuracy and therefore, they might be used to efficiently control an HVAC system. [ABSTRACT FROM AUTHOR]

Heat, ventilation, and air conditioning (HVAC) systems are some of the most energy-intensive equipment in buildings and their faulty or inefficient operation can significantly increase energy waste. Non-Intrusive Load Monitoring (NILM), which is a software-based tool, has been a popular research area over the last few decades. NILM can play an important role in providing future energy efficiency feedback and developing fault detection and diagnosis (FDD) tools in smart buildings. Therefore, the review of NILM-based methods for FDD and the energy efficiency (EE) assessment of HVACs can be beneficial for users as well as buildings and facilities operators. To the best of the authors' knowledge, this paper is the first review paper on the application of NILM techniques in these areas and highlights their effectiveness and limitations. This review shows that even though NILM could be successfully implemented for FDD and the EE evaluation of HVACs, and enhance the performance of these techniques, there are many research opportunities to improve or develop NILM-based FDD methods to deal with real-world challenges. These challenges and future research works are also discussed in-depth. [ABSTRACT FROM AUTHOR]

Energy has become the backbone of humanities daily activities. Heating, ventilating, and air conditioning systems (HVAC), which consume around 39% of energy in the residential sector, have turned into an essential constituent for providing fresh air, especially after COVD-19, not only in hospitals but also in any simple construction. Thus, decreasing this percentage or recovering part of the energy lost is an essential issue in today's energy management scenarios. In this context, the present manuscript suggests a comprehensive review, classifications, critical analysis, and potential recommendations for energy recovery in air conditioning systems. It classifies energy recovery into two main categories: using lost energy for external uses, such as heating domestic water, or with other devices; and using lost energy for internal uses, such as the hot airflow which can be reused again for increasing efficiency of HVAC. In addition, this paper presents a summary of previous research and undertakes a review of the devices used for recovering energy. Furthermore, this review identifies superior devices in terms of climate and weather conditions. These objectives are accomplished by investigating around 190 published papers to conclude that energy recovery devices show a considerable effect on energy consumption in HVAC, mainly the heat pipe, fixed plate, and rotary wheel devices. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid algorithm to solve the optimal energy dispatch of an ice storage air-conditioning system. Based on a real air-conditioning system, the data, including the return temperature of chilled water, the supply temperature of chilled water, the return temperature of ice storage water, and the supply temperature of ice storage water, are measured. The least-squares regression (LSR) is used to obtain the input-output (I/O) curve for the cooling load and power consumption of chillers and ice storage tank. The objective is to minimize overall cost in a daily schedule while satisfying all constraints, including cooling loading under the time-of-use (TOU) rate. Based on the Radial Basis Function Network (RBFN) and Ant Colony Optimization, an Ant-Based Radial Basis Function Network (ARBFN) is constructed in the searching process. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the economic dispatch of ice storage air-conditioning systems, and offering greater energy efficiency in dispatching chillers. [ABSTRACT FROM AUTHOR]

The equipment energy efficiency improvement policy (EEEIP) is one of the important measures of energy conservation and emission reduction in various countries. However, due to the simultaneous implementation of variety policies, the effect of the single policy cannot be clearly reflected. In this paper, a method of identification and evaluation of EEEIP was proposed, and the application was verified by analyzing the example of EEEIP in Japan (Top Runner policy, TRP). Firstly, through the factor decomposition model, this paper studied the energy conservation and emission reduction potential of this policy area in Japan. Then, the TRP was identified by using moving windows and correlation analysis, and the impact of specific equipment in TRP was analyzed. Finally, through the calculation of the rebound effect of the carbon footprint (REC), this paper analyzed the energy consumption and emission reduction effects of TRP in the short-term and whole life cycle. It showed that the policy has a good effect in tertiary industry and transportation, while the effect in residential is poor. For life cycle, the TRP of air conditioning and passenger car can bring better CO2 emission reduction effect, but the emission reduction effect of lighting is basically offset. [ABSTRACT FROM AUTHOR]

This paper aims to determine cost optimality between heating, ventilation and air conditioning (HVAC) systems operating with air to water heat pumps (AWHP) and water to water heat pumps (WWHP). The analysis is performed for a certain number of heat pump units with fixed and variable capacity made by four manufacturers available on European market. Simulations are performed in Trnsys software. The results show that heat pump partial load efficiency should not be neglected in analysis of application while the difference in energy consumption and costs can be up to 17%. The requirement for performing analysis on a wider range of units is indicated, especially when heat pump systems with different sources are considered. HVAC system with AWHP units with capacity control is a cost optimal solution for case study nursery building operating on the Croatian coast. The application of the photovoltaic (PV) array sized to cover nonrenewable part of electricity consumed in HVAC system has a return period of 12 years. It is determined that seasonal efficiency indicators from relevant European database do not support unit operation. [ABSTRACT FROM AUTHOR]

To meet the rapid-growing demand for electricity in Kuwait, utility planners need to be informed on the energy consumption to implement energy efficiency measures to manage sustainable load growth and avoid the high costs of increasing generation capacities. The first step of forecasting the future energy profile is to establish a baseline for Kuwait (i.e., a business-as-usual reference scenario where no energy efficiency incentives were given and the adoption of energy efficient equipment is purely market-driven). This paper presents an investigation of creating a baseline end-use energy profile until 2040 for the residential sector in Kuwait by using a bottom-up approach. The forecast consists of mainly two steps: (1) Forecasting the quantity of the residential energy-consuming equipment in the entire sector until 2040 where this paper used a stock-and-flow model that accounted for the income level, electrification, and urbanization rate to predict the quantify of the equipment over the years until 2040, and (2) calculate the unit energy consumption (UEC) for all equipment types using a variety of methods including EnergyPlus simulation models for cooling equipment. By combining the unit energy consumption and quantity of the equipment over the years, this paper established a baseline energy use profile for different end-use equipment for Kuwait until 2040. The results showed that the air conditioning loads accounted for 67% of residential electrical consumption and 72% of residential peak demand in Kuwait. The highest energy consuming appliances were refrigerators and freezers. Additionally, the air conditioning loads are expected to rise in the future, with an average annual growth rate of 2.9%, whereas the lighting and water heating loads are expected to rise at a much lower rate. [ABSTRACT FROM AUTHOR]

Lowering the condensing temperature of the Refrigeration and Air-conditioning (RAC) system has been proven to effectively increase the system's Coefficient of Performance (COP). This paper revolves around evaluating the energy-saving generated by applying a Thermal Diode Tank (TDT) in the RAC systems. The TDT is a novel invention, which is an insulated water tank equipped with gravity heat pipes. If the TDT was placed outdoors overnight, its inside water would theoretically be at the minimum ambient temperature of the previous night. When the TDT water is used to cool the condenser of RAC systems that operate during the daytime, a higher COP of this TDT assisted RAC (TDT-RAC) system could be achieved compared with the baseline system. In this study, a steady-state performance simulation model for TDT-RAC cycles has been developed. The model reveals that the COP of the TDT-RAC cycle can be improved by 10~59% over the baseline cycle depending on the compressor types. The TDT-RAC cycle with a variable speed compressor can save more energy than that with a fixed speed compressor. In addition, TDT-RAC cycles can save more energy with a higher day/night ambient temperature difference. There is a threshold tank size for a given TDT-RAC cycle to save energy, and the energy-saving can be improved by enlarging the tank size. A desk-top case study based on real weather data for Adelaide in January 2021 shows that 9~40% energy could be saved by TDT-RAC systems every summer day on average. [ABSTRACT FROM AUTHOR]

This paper presents a reversible heat pump based on CO2 as the refrigerant, able to provide heating, cooling, and domestic hot water to high energy demand buildings. The unit was developed and tested under the EU H2020 project MultiPACK, which has the main goal of assuring the market about the feasibility, reliability, and energy efficiency of CO2 integrated systems for heating and cooling and promoting a fast transition to low environmental impact solutions. Within the project, the confidence raising was performed by installation and monitoring of fully integrated state-of-the art CO2 systems in the Southern European Climate. With the aim of predicting the unit behaviour under variable load and boundary conditions, a dynamic model of the entire unit was developed with commercial software, considering actual components and the implemented control system and it was validated with experimental data, collected at the factory's lab before commissioning. The validation against experimental data collected during operation as a heat pump demonstrated a maximum percentage difference between the experimental and predicted value of gas–cooler heat flow rate equal to +5.0%. A preliminary comparison with the experimental data in chiller configuration is reported, however further development was required to achieve a satisfactory validation. Lastly, the numerical model was utilized to simulate a typical operation in heat pump configuration with the system coupled with a hot water tank storage for the production of domestic hot water and space heating; the model predicts higher COP when operating in domestic hot water operation due to the lower water inlet temperature. [ABSTRACT FROM AUTHOR]

This paper puts forward a methodology for calculating the duration and energy efficiency of regimes for autoclave steaming of wooden prisms for veneer production at limited heat power of the steam generator, depending on the dimensions of the prism's cross section, wood moisture content, and loading level of the autoclave. The methodology is based on the use of two personal mathematical models: the 2D non-linear model of the temperature distribution in non-frozen wooden prisms subjected to steaming and subsequent conditioning in an air medium, and the model of the non-stationary heat balance of autoclaves for steaming wood materials. Using the suggested methodology, the calculation and research into the duration and energy efficiency of regimes for heating of beech prisms have been carried out. The variables used were an initial temperature of 0 °C, cross-section dimensions 0.3 × 0.3 m, 0.4 × 0.4 m, and 0.5 × 0.5 m, moisture content of 0.4, 0.6, and 0.8 kg·kg−1, during their steaming in an autoclave with a diameter of 2.4 m, length of 9.0 m and loading level of 40, 50, and 60% at a limited heat power of the steam generator, equal to 500 kW. It has been determined that the duration of the autoclave steaming regimes, at a loading level of 50% being most often used in the practice beech prisms with moisture of 0.6 kg·kg−1, does not exceed 9 h, 13 h, and 20 h for prisms with cross-section 0.3 × 0.3 m, 0.4 × 0.4 m, and 0.5 × 0.5 m, respectively. This duration is less than half of the corresponding duration of the steaming regimes at atmospheric pressure. The energy needed for warming up such prisms themselves does not exceed 60, 65, and 69 kWh·m−3, respectively, and the energy consumption of the whole autoclave then is equal to about 90, 99, and 105 kWh·m−3, respectively. The energy efficiency of the autoclave steaming regimes changes between 62.2% and 68.8% for the studied ranges of the influencing factors and it turns out to be more than 2–3 times larger in comparison with the efficiency of the steaming at atmospheric pressure. The methodology can be used for various calculations with ANSYS and to create the software for systems used for computing and model-based automatic realization of energy-efficient regimes for autoclave steaming of different wood materials from various species. This could be useful in developing similar methodologies in different areas of thermal treatment at increased pressure of various capillary-porous materials of plant or technical origin. [ABSTRACT FROM AUTHOR]

The paper analyzes and compares the perspectives for reducing the energy consumption associated to the operation of Heating Ventilation and Air Conditioning system for climatic control of large-size non-residential buildings. Three different control strategies are considered comparing the use of boiler and heat pumps as heating systems and analyzing the use of demand-controlled ventilation, operating on the effective occupancy of the building. The control strategies are applied to two different educational buildings with shapes representative of typical educational structures. The results of the analysis show how the energy consumption can be reduced up to 70%, shifting from the actual values of the energy intensity of over 300 kWh/m2 for year to values of less than 100 kWh/m2 per year. The significance of the energy savings achieved in such different buildings has led to the identification of a possible benchmark for HVAC systems in the next future years which could help reach the environmental targets in this sector. [ABSTRACT FROM AUTHOR]

The paper provides a methodology for the optimal control of heating, ventilation, and air conditioning (HVAC) systems used in public buildings, with the purpose of obtaining high comfort and safety standards along with energy efficiency. The combination of the two concurrent objectives of minimizing energy use and guaranteeing high standards of occupant comfort is obtained by means of multi-objective optimization, in which a comfort model is combined along with a dynamic energy model of the building. The use of dynamic setpoints for the HVAC and the inclusion of comfort indicators represent a step forward, compared to the current design and operation procedures suggested by technical standards. The utilization of the proposed methodology is tested with reference to a case study, represented by an academic building used by the University of Pisa for educational purposes, whose extensive and variable occupancy can help to emphasize the importance of comfort in the operation of HVAC systems in different climatic conditions and with different occupancy profiles. We show how this optimization brings interesting results in terms of energy-saving (up to 30%), obtaining an increased comfort level (of more than 25%) compared to the operating conditions suggested by technical standards. [ABSTRACT FROM AUTHOR]

Air conditioning accounts for up to 50% of energy use in buildings. Increased air-conditioning-system installations not only increase total energy consumption but also raise peak load demand. Desiccant evaporative cooling systems use low-grade thermal energy, such as solar energy and waste heat, instead of electricity to provide thermal comfort. This system can potentially lead to significant energy saving, reduction in carbon emissions, and it has a low dew-point operation and large capacity range. Their light weight, simplicity of design, and close-to-atmospheric operation make them easy to maintain. This paper evaluates the applicability of this technology to the climatic conditions of Brisbane, Queensland, Australia, specifically for the residential sector. Given the subtropical climate of Brisbane, where humidity levels are not excessively high during cooling periods, the numerical study shows that such a system can be a potential alternative to conventional compression-based air-conditioning systems. Nevertheless, the installation of such a system in Brisbane's climate zone requires careful design, proper selection of components, and a cheap heat source for regeneration. The paper also discusses the economy-cycle options for this system in such a climate and compares its effectiveness to natural ventilation. [ABSTRACT FROM AUTHOR]

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users' comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model. [ABSTRACT FROM AUTHOR]

Evaporative cooling is a green, energy-efficient cooling technology adopted in hot and dry regions, which has wider application in the field of air-conditioning systems. Outdoor meteorological parameters have a great influence on the operation mode and control strategy of evaporative cooling air-conditioning systems, and the system load distribution and system configuration will be affected. This paper aims at investigating the load distribution of semi-central evaporative cooling air-conditioning systems under the condition of hourly outdoor meteorological parameters. Firstly, this paper introduced the design partition, operation mode, controlling strategy and load distribution method on semi-central evaporative cooling air-conditioning system. Then, taking an office building in Lanzhou (China) as an example, the evaporative cooling air-conditioning system was divided into five regions and the load distribution was simulated by TRNSYS (The Transient Energy System Simulation Tool) under the condition of hourly outdoor meteorological parameters. Finally, the results have shown that the evaporative cooling air-conditioning system can provide 25.46% of the building loads, which was of great significance to reduce the energy consumption of air-conditioning system. [ABSTRACT FROM AUTHOR]

Air quality and energy consumption are among the top ten environmental priorities in seaports as stated by the European Sea Ports Organization. Globally, it is estimated that 15% of energy consumption can be attributed to refrigeration and air conditioning systems in fishing activities. There is a real need to understand energy usage in fishery ports to help identify areas of improvements, with a view to optimize energy usage and minimize carbon emissions. In this study, we elaborate on ways in which a simulation capability can be developed at the community level with a fishery port, using a real-world case study seaport in Milford Heaven (Wales, UK). This simulation-based strategy is used to investigate the potential of renewable energy, including local solar farms, to meet the local power demand. This has informed the development of a simulation-based optimization strategy meant to explore how smart energy communities can be formed at the port level by integrating the smart grid with the local community energy storage. The main contribution of the paper involves a co-simulation environment that leverages calibrated energy simulation models to deliver an optimization capability that (a) manages electrical storage within a district an environment, and (b) promotes the formation of energy communities in a fishery port ecosystem. This is paving the way to policy implications, not only in terms of carbon and energy reduction, but also in the formation and sustained management of energy communities. [ABSTRACT FROM AUTHOR]

Rooftop gardens ona building have proved to be a good way to improve its storm water management, but many other benefits can be obtained from the installation of these systems, such as reduction of energy consumption, decrease of the heat stress, abatement on CO2 emissions, etc. In this paper, the effect from the presence of these rooftop gardens on abuilding's energy consumption has been investigated by experimental campaigns using a green roof ona public building in a Mediterranean location in Spain. The obtained results demonstrate a substantial improvement by the installation of the green roof onthe building's cooling energy demand for a standard summer day, in the order of 30%, and a reduction, about 15%, in the heating energy demand for a winter day. Thus, given the longer duration of the summer conditions along the year, a noticeable reduction on energy demand could be obtained. Simulation analysis, using commercial software TRNSYS code, previously calibrated using experimental data for typical summer and winter days, allows for the extrapolation to the entire year of these results deducing noticeable improvement in energy efficiency, in the order of 19%, but with an increase of 6% in the peak power during the winter period. [ABSTRACT FROM AUTHOR]

About 2/3 of the combustion energy of internal combustion engine (ICE) is lost through the exhaust and cooling systems during its operation. Besides, automobile accessories like the air conditioning system and the radiator fan will bring additional power consumption. To improve the ICE efficiency, this paper designs some coupled thermal management systems with different structures which include the air conditioning subsystem, the waste heat recovery subsystem, engine and coolant subsystem. CO2 is chosen as the working fluid for both the air conditioning subsystem and the waste heat recovery subsystem. After conducting experimental studies and a performance analysis for the subsystems, the coupled thermal management system is evaluated at different environmental temperatures and engine working conditions to choose the best structure. The optimal pump speed increases with the increase of environmental temperature and the decrease of engine load. The optimal coolant utilization rate decreases with the increase of engine load and environmental temperature, and the value is between 38% and 52%. While considering the effect of environmental temperature and road conditions of real driving and the energy consumption of all accessories of the thermal management system, the optimal thermal management system provides a net power of 4.2 kW, improving the ICE fuel economy by 1.2%. [ABSTRACT FROM AUTHOR]

After the first concerns about global energy consumption around the 70 s, some standards were established with the aim of improving the energy efficiency in buildings. In this context, according to the US Department of Energy, heating, ventilation, and air conditioning (HVAC) systems comprise about 50% of the building energy consumption and 20% of total consumption in the United States. Energy consumption of this kind of equipment depends on several factors, one of the most important factors being the heat transfer through the building envelope. In the walls, hollow concrete blocks have become common in recent years due to advantages such as their strength, their space for pipes and wiring, and fast construction, among others. In order to reduce the thermal load, several forms of insulation were tested on these constructive elements. Thus, effects of the low emissivity, insulating material, radiant shields, and baffles on the inner cavities of the blocks are presented in this paper. Effects of temperature differences between the exterior surfaces on the thermal transmittance and on the combined average heat transfer coefficient inside the cavities are also shown. [ABSTRACT FROM AUTHOR]