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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]

In view of the prominent and intractable problems of frequent high temperature alarms of the communication equipment and high energy consumption of air conditioners in Zhengzhou communication outdoor cabinets, a temperature control system with heat pipe as the main part and air conditioner as the auxiliary part is proposed in this paper. A prototype is developed and its applications are discussed. The annual field test shows the system works well, and it has a remarkable energy saving efficiency. [ABSTRACT FROM AUTHOR]

• To improve data center performance, it must be efficiently cooled. • This paper undertakes a detailed examination of the most recent cooling optimization techniques. • Several methods are discussed like regular air conditioning, free cooling, and liquid cooling. • An assessment of overhead and underfloor air delivery technologies is presented. • The effects of baffles, variable airflow control, and server placement are investigated. In order to increase data centers' efficiency and performance, a proper cooling system should be applied. This article provides a comprehensive assessment which explores current cooling optimization technologies for data centers. Standard traditional technologies like air conditioning, free cooling, and liquid cooling are investigated. Their limitations as well as proposed improvements are thoroughly discussed. The article also delves into underfloor and overhead air supply technologies, presenting their advantages and drawbacks. The review introduces novel approaches at the rack level cooling, including baffles, variable airflow management, and optimal server placement. Key findings stress the efficacy of optimized airflow systems and innovative rack-level cooling, underlining their role in reducing energy consumption and enhancing overall performance. Notably, potential energy savings of up to 67.2 % compared to traditional methods are demonstrated. The adoption of advanced cooling technologies, such as direct and indirect natural cooling, liquid-cooling cold plates, submersion, heat pipe, and thermosiphon-based cooling, exhibits promising energy efficiency gains and reduced Power Usage Effectiveness (PUE) values. Research into content-specific efficiency enhancements, adaptable energy-saving tactics, and all-encompassing server power consumption models is necessary. To work toward future sustainable, energy-efficient data centers, researchers should investigate how CRACs and server fans interact, find ways to integrate thermal and power management, and how artificial intelligence can be used to cool servers in an eco-friendly way. [ABSTRACT FROM AUTHOR]

In buildings, air conditioning and mechanical ventilation (ACMV) systems are the major shareholders of overall energy consumption. Energy-efficient designs for ACMV systems in building applications are therefore needed. While designing an efficient ACMV system, consideration must be given to the growing concerns of enhanced thermal comfort and improved indoor air quality. The variable refrigerant flow (VRF) air-conditioning system is a widely adopted alternative to the existing building cooling systems due to the higher energy efficiency and individualized temperature control feature. However, it still suffers from shortcomings such as no outdoor air induction for ventilation and higher initial cost. Therefore, this paper reviewed the variable refrigerant flow and mechanical ventilation/air distribution systems, their integrated designs for non-residential buildings, performance evaluation and control optimization of the integrated systems, VRF systems' faults detection and diagnosis, current application of the VRF systems, and associated challenges. Together with these all, some advanced buildings' cooling techniques and improvements toward nearly/net-zero energy buildings are briefly discussed. Indoor thermal comfort models and criteria for different climates are also presented for an in-depth understanding of the VRF integrated mechanical ventilation designs. The literature survey shows that the supply air temperature and airflow rate are foremost in parameters that can be optimized in VRF integrated ventilation design as they greatly reduce the energy consumption. Further, policies on elevated indoor temperatures in air-conditioned buildings to mitigate their carbon footprint are strictly being implemented. Therefore, this review provides an insight to the researchers for further improvement in the integrated design and control optimization of the parameters involved. A paradigm shifts from the conventional compression-based electric-powered air conditioning systems to the renewable energy driven advanced air conditioning technologies which is also an emerging research area to be focused on achieving the target of nearly/net-zero energy buildings. [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]

Closed-loop control of commercial building heating, ventilating, and air conditioning (HVAC) for demand response requires measurements used as feedback to the controllers. Demand response effectiveness is usually measured as a power deviation from baseline, but the building automation system (BAS) does not usually collect power measurements, and whole-building electric meters typically measure power at intervals of 15 min, which may be too slow for some types of demand response. Demand response strategies are sometimes focused on components of building HVAC systems, e.g., the response of supply/return fans to temperature set-point changes, but these components are usually not submetered. Fan power can be estimated from physics-based models leveraging BAS data, e.g., airflow measurements; but our ability to effectively close the loop on these estimates is not clear. In this paper, we introduce a massive dataset that contains both submetered fan power data and BAS data for several building HVAC systems during typical operation and demand response events. Through a case study we show that models leveraging BAS data alone do not provide accurate estimates of fan power during event transients, making it unlikely that closed-loop control of commercial building HVAC components for demand response would be effective using BAS data alone. This demonstrates the value of submetering HVAC components. More broadly, our dataset will enable future research bridging the gap between building control and power systems research. [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]

For normal operating conditions of the centrifugal fan, the manufacturers provide operating curves (total pressure of the fan, static pressure of the fan, power, and efficiency) for standard air conditions. However, when a fan operates with air at different, usually elevated temperatures, their operating curves can significantly differ. As a result, the fan efficiency decreases, and the energy efficiency of the fan may be reduced. Fan operation in such air conditions also affects its acoustic characteristics. This paper investigates two types of centrifugal fans: with forward curved and backward inclined blades. The main goal was to determine how their operating and acoustic characteristics change with the change in air temperature. For this analysis, low pressure centrifugal fans are used, for which we already had measured operating and acoustic characteristics. The influence of the type of impeller and the curvature of the blades play an important role in the operation of the fan. For numerical investigation, ANSYS CFX software was used, and obtained results show excellent agreement with experimental measurements. [ABSTRACT FROM AUTHOR]

Active adjustment of the airflow organization in data centers (DC) has been increasingly applied due to its superior adjustment performance. But for now, the main focus is on the airflow distribution optimization in the horizontal direction, while few research has been conducted to investigate the additional energy consumption of active method. This paper introduced the jet-induced ventilation into under-floor air distribution (UFAD), then the coupling air supply system (CASS) was established. Taking the CASS data center as an example, this paper focuses on the impact of the active adjustment method on both airflow organization and energy consumption of the DC, and analyzes the energy consumption of the system while meeting the security operations of the thermal environment. The results showed that the incorporation of jet-induced ventilation effectively improved the airflow distribution uniformity, while the active adjustment of the airflow organization in the DC has little effect on the overall energy consumption. On the premise of meeting the demand of thermal environment, the energy consumption of the CRAC (Computer room air conditioning) units in the CASS is reduced and the change of the overall energy consumption is small, while the total energy consumption increased with the increase of airflow uniformity. [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]

Low global warming potential hydrofluoroolefin-1234yf (R1234yf) is an alternative green refrigerant for the automotive air conditioning (AAC) system to replace the existing R134a refrigerant. However, the low energy efficiency of the AAC system using R1234yf has been a significant obstacle to its wider use. This paper describes using Al2O3–SiO2/DEC PAG nanolubricant to enhance the performance of the AAC-R1234yf system. The hybrid nanolubricant was created by a two-step preparation process. The experiment was undertaken at volume concentrations up to 0.05% and under various operating conditions. The stability test shows that the hybrid nanolubricant remained in superior stability after 6 months, with minimum particle aggregation and sedimentation. The AAC-Al2O3–SiO2/DEC PAG system achieved the best performance at 0.03% volume concentration with an average increment of 12.01%. This outcome was contributed by the increase of the AAC evaporator's heat absorb value by 2.8% and the reduction of the compressor work with an average decrease of 7.7%. In conclusion, a 0.03% volume concentration of Al2O3–SiO2/DEC PAG nanolubricant in the AAC compressor was recommended for optimum system performance and energy saving. [ABSTRACT FROM AUTHOR]

In order to reduce global energy consumption, energy-efficient, green and smart buildings should be built. In addition to the application of other energy efficiency measures, effective management of heating, ventilation and air conditioning (HVAC) systems are also required. High-quality management and control of those systems ensures optimal occupant comfort, proper operation, rational energy consumption, and a positive impact on the environment. This is especially important for large buildings with complex systems such as hotels. As a contribution to the creation of appropriate tools for the management and control of HVAC systems in smart buildings, this paper presents the results of the development of a detailed dynamic simulation model based on data collected from a smart room system in a hotel in Zagreb, Croatia. The smart room system provides historical data on a set and actual room temperatures, occupancy schedule, windows openings, fan coil unit (FCU) operating status, fan speed, control valve opening, and heating or cooling modes with a time step of 5 minutes. The simulation model based on TRNSYS software uses part of the available data and predicts the room temperatures. A comparison of the predicted and measured temperatures at each time step shows that the deviations of the manually calibrated model are within acceptable standardized limits. The developed model has been applied to identify major anomalies in the operation of the FCU system, yielding promising results. [ABSTRACT FROM AUTHOR]

Over the past three years, regulations have been implemented to combine natural ventilation (NV) and air conditioning to mitigate the risk of disease transmission, particularly in response to the COVID-19 outbreak. As we know, simultaneous use of NV and air conditioning can make it challenging to achieve indoor thermal comfort. This paper aims to analyze the effect of NV on the air conditioning's cooling and heating load in a classroom through simulation. A simulation model was developed using EnergyPlus software with an OpenStudio interface software. Simulation results demonstrate that continuous use of NV alongside an air conditioner increases the cooling load from 1.06 to 1.75 times during summer and a 1.54 to 9.49 times heating load increase during winter. On the other hand, intermittent NV every hour results in a cooling load increase from 1.05 to 1.46 times in summer and a heating load increase from 1.13 to 4.63 times in winter. Moreover, employing NV based on the outside air temperature can reduce the cooling load at the air conditioner with set-point 26℃—28℃ from 0.94 to 0.88 times. The outcomes of this study are expected to serve as a reference for determining strategies that effectively combine NV and air conditioning to meet various needs without causing a significant increase in energy consumption. Additionally, the results are expected to be useful for reducing AC energy consumption in extremely hot and cold weather with some strategies of NV application. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of accurately reflecting the actual effect of the engineering application of the data center’s energy consumption evaluation results. In this paper, on the basis of the existing data center energy efficiency evaluation index, from the data center, data center air conditioning system, indirect evaporative cooling air conditioning equipment in three dimensions, respectively, put forward the data center indirect evaporative cooling air conditioning PUE coefficient, data center air conditioning energy utilization efficiency, indirect evaporative cooling air conditioning equipment refrigeration density and other evaluation indicators. Using the new evaluation indexes as well as the assignment analysis method, case studies were conducted on data centers with different cooling systems. The results show that the indirect evaporative cooling air-conditioning system has obvious advantages of power and water saving compared with the traditional chilled water system, but its refrigeration density index still has much room for improvement. The proposal of the new index improves the energy efficiency evaluation system of data centers and can lead to further improvement of the performance quality of related air conditioning products. [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]

Energy efficiency and thermal comfort levels are key attributes to be considered in the design and implementation of a Heating, Ventilation and Air Conditioning (HVAC) system. With the increased availability of Internet of Things (IoT) devices, it is now possible to continuously monitor multiple variables that influence a user's thermal comfort and the system's energy efficiency, thus acting preemptively to optimize these factors. To this end, this paper reports on a case study with a two-fold aim; first, to analyze the performance of a conventional HVAC system through data analytics; secondly, to explore the use of interpretable machine learning techniques for HVAC predictive control. A new Interpretable Machine Learning (IML) algorithm called Permutation Feature-based Frequency Response Analysis (PF-FRA) is also proposed. Results demonstrate that the proposed model can generate accurate forecasts of Room Temperature (RT) levels by taking into account historical RT information, as well as additional environmental and time-series features. Our proposed model achieves 0.4017 °C and 0.9417 °C of Mean Absolute Error (MAE) for 1-h and 8-h ahead RT prediction, respectively. Tools such as surrogate models and Shapley graphs are employed to interpret the model's global and local behaviors with the aim of increasing trust in the model. [ABSTRACT FROM AUTHOR]

• FDD methods and process discussed in detail. • IoT and cloud technology for FDD is discussed. • Presents the detailed discussion on fault modeling. • Challenges and future prospects of FDD systems are discussed. This review study examines the latest research and developments in the fault detection and diagnostics of Heating Ventilation and Air Conditioning (HVAC) systems. This review describes the basics of Fault detection and diagnostics in the HVAC systems, and the methods developed for the FDD have been discussed in detail. Machine learning methods have become prevalent in the FDD. Supervised and unsupervised machine learning methods have been discussed. Data preprocessing and feature selection are the two essential steps of the FDD process using machine learning. Fault prognosis has also been discussed in brief. Further, fault modeling and its applications in the FDD have been covered. Various approaches have been used to model the different faults in HVAC systems. This paper reviews FDD systems based on four aspects, i.e., detection, diagnostics, prognostics, and modeling of faults. Then this review provides a comparative study of different FDD methods. Finally, the paper discusses future challenges for the more efficient FDD systems to reduce the energy consumption of the HVAC systems. [ABSTRACT FROM AUTHOR]

The energy consumption of a heating, ventilation, and air conditioning (HVAC) system represents a large amount of the total for a commercial or civic building. In order to optimize the system performance and to increase the comfort of people living or working in a building, it is necessary to monitor the relevant parameters of the circulating air flux. To this end, an array of sensors (i.e., temperature, humidity, and CO2 percentage sensors) is usually deployed along the aeraulic ducts and/or in various rooms. Generally, these sensors are powered by wires or batteries, but both methods have some drawbacks. In this paper, a possible solution to these drawbacks is proposed. It presents a wireless sensor node powered by an Energy Harvesting (EH) device acted on by the air flux itself. The collected data are transmitted to a central unit via a LoRa radio channel. The EH device can be placed in air ducts or close to air outlets. [ABSTRACT FROM AUTHOR]

The principle of piezoelectricity has been used to design the micro-electro-mechanical system-based piezoelectric energy harvester for generating electrical energy from surrounding vibrations. The huge popularity of these energy harvesters (EHs) is due to their small size. This paper designs the EH for the heating, ventilation, and air conditioning system. Different cantilever structures with different shapes of the same fabricated area have been designed and investigated at an excited acceleration of 1 g (9.8 ms−2). The multi-perforated cantilever structure has the better stress distribution within the cantilever structure and generates better output voltage and power than their basic design. The proposed tapered multi-perforated cantilever structure has the least value of the resonant frequency of 98.59 Hz which is 31.13% less than the rectangular cantilever structure. The proposed structure generates the output voltage of 14 V which is 91.78% higher than the basic rectangular cantilever structure. It also generates the average output power of 7.15 µW which is 213.59% higher than basic rectangular cantilever structure. [ABSTRACT FROM AUTHOR]

In this paper, the feasibility of two low global warming potential refrigerants R513A and R1234yf as alternatives to R134a was tested in an automotive air conditioning system in winter. The test results showed that the heating capacities of R513A and R1234yf were 2% and 4% lower than that of R134a, and their COP were 3-9% and 6-19% lower than that of R134a, respectively. It revealed that R513A can be used as a transition product to replace R134a, and R1234yf required further improvements to improve the energy efficiency. [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]

In their quest to enhance energy efficiency and carbon footprint management, many enterprises are turning their attention towards optimizing their facilities through amplified data monitoring and innovative control methods. In this context, this research highlights a practical solution in a commercial building located in the United States. It explains the creation, implementation, and costs of a universally applicable model predictive control (MPC) framework for the building's heating, ventilation, and air conditioning (HVAC) system. The paper elaborates on both the hardware and software used to accumulate pertinent data and the formulation of the MPC system. This approach leverages cloud-based microservices and can be seamlessly integrated into current building management systems. In this regard, this paper presents three innovative strategies: Proportional–integral (PI)+Preheating, MPC, and occupancy-based control. These scenarios were designed to improve energy efficiency and thermal comfort significantly by reducing temperature fluctuations and adhering more closely to the setpoint temperature. The implementation of these strategies resulted in substantial energy savings, with reductions in energy consumption of 12.83%, 19.21%, and 14.98%, respectively. • A novel cloud-based MPC scheme is proposed for controlling the educational building HVAC system. • Real-world case installation details are provided with a focus on cost and sensor information. • A comprehensive discussion on thermal modeling, occupants' comfort, and control performance. [ABSTRACT FROM AUTHOR]

With recent energy and environmental crises, the energy consumption prediction of air conditioning (AC) is essential. Notably, existing general prediction systems use the air temperature, real-time AC usage, time, occupancy, and AC historical energy consumption as inputs and employ feedforward neural networks. However, such systems face certain challenges. First, the input system may be difficult to reflect the real-time interaction among the building environment, occupants, and energy system; Second, the algorithms need to be upgraded to further improve accuracy, speed up convergence, and avoid overfitting. his paper introduces a novel parameter (group occupant behavior probability, GOBP) in the input system; and uses swarm intelligence algorithms with chaotic mapping and adaptive weight adjustment as optimization with LSTM and BP as the basic algorithms. The verification results obtained from five different buildings indicate the following: (1) The novel prediction system improved prediction performance with 20.08%–78.37 % of MAPE and 3.99%–53.28 % of CV-RMSE, however, it will increase 148.70s–1065.11s of calculation time. (2) For accuracy, using GOBP will improve the 0.30%–51.87 % and 0.18%–64.77 % by using swarm intelligence optimization algorithms. (3) This paper discusses the priority of selecting suitable inputs, algorithms, and optimization to combine a whole prediction system. • The definition of a simplified model using the group occupant behavior probability is proposed. • The chaotic mapping and adaptive weight adjustment are used for optimizing the swarm intelligent algorithms. • A reconstituted AC energy consumption prediction system is proposed with novel inputs and improved algorithms. • The reconstructed prediction system demonstrates improved prediction accuracy and data acquisition difficulty\. • The best combination of inputs and algorithms is discussed based on 60 groups of comparative experiments. [ABSTRACT FROM AUTHOR]

The optimal control of a system governed by an elliptic equation has been widely applied in engineering, and it requires twice differentiability. Furthermore, the optimal control cannot effectively deal with unsmooth boundaries. Now, the condition will not be maintained for a long time because this paper suggests a fractal optimal control of a system governed by a fractal variational principle to deal with unsmooth boundaries. The new optimal theory has obvious advantages in low differentiability for an unsmooth boundary problem. The air conditioning in a room is used as an example to show how to maintain maximum comfortableness of the workplace and maximum efficiency of energy saving. [ABSTRACT FROM AUTHOR]

Energy efficient control of thermal comfort has been already an important part of residential heating, ventilation, and air conditioning (HVAC) systems. However, the optimisation of energy saving control for thermal comfort is not an easy task due to the complex dynamics of HVAC systems, the dynamics of thermal comfort and the trade-off between energy saving and thermal comfort. To solve the above problem, we propose a deep reinforcement learning-based thermal comfort control method in multi-zone residential HVAC. In this paper, firstly we design a SVR-DNN model, consisting of Support Vector Regression and a Deep Neural Network to predict thermal comfort value. Then, we apply Deep Deterministic Policy Gradient (DDPG) based on the output of the SVR-DNN model to achieve an optimal HVAC thermal comfort control strategy. This method can minimise energy consumption while satisfying occupants' thermal comfort. The experimental results show that our method can improve thermal comfort prediction performance by 20.5% compared with DNN; compared with deep Q-network (DQN), energy consumption and thermal comfort violation can be reduced by 3.52% and 64.37% respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to make short-term predictions for the three kinds of primary energy consumption of power, lighting, and ventilated air conditioning in the metro station. First, the paper extracts the five main factors influencing metro station energy consumption through the kernel principal component analysis (KPCA). Second, improved genetic-ant colony optimization (G-ACO) was fused into the BP neural network to train and optimize the connection weights and thresholds between each BP neural network layer. The paper then builds a G-ACO-BP neural model to make short-term predictions about different energy consumption in the metro station to predict the energy consumed by power, lighting, and ventilated air conditioning. The experimental results showed that the G-ACO-BP neural model could give a more accurate and effective prediction for the main energy consumption in a metro station. [ABSTRACT FROM AUTHOR]

To avoid unnecessary energy waste due to the single temperature setpoints of the heating, ventilation, and air conditioning (HVAC) system during the seasonal variation period, this study proposed a long-short-term memory (LSTM) neural network to predict and control the temperature setpoint. The thermal comfort, cooling rate, and heating rate were predicted with outdoor environment parameters and occupant count. A large scale of operation data was collected from the EnergyPlus simulation, which was previously developed based on the characteristics of a real case study house. Different kinds of input characteristics were offered to test the stable use of LSTM and other artificial neural networks. This paper discusses the development of a Matlab EnergyPlus co-simulation to predict and control the temperature setpoints of a variable air volume system, especially the relationship between temperature setpoint and energy consumption. The simulation results indicate the advantages of the LSTM prediction of energy consumption and the potential for energy saving with predictive control. [ABSTRACT FROM AUTHOR]

The thermal management system (TMS) in electric vehicles (EVs) is a comprehensive system that integrates an air conditioning system for the cabin, a temperature control system for the battery, and a cooling system for the motor. The currently used PI control strategy can only meet the basic TMS functions and cause high energy consumption. In this paper, we present a novel model predictive control (MPC) strategy for the TMS to optimize operational performance in real time. Different from the independent PI control for the individual components, MPC can predict future operation conditions and provide the optimal operating inputs in advance. A complete control-oriented model for MPC is developed, and the MPC strategy is designed to minimize the total power consumption of the TMS under the control-oriented model and constraints. The evaluation is carried out under several cases including the fixed ambient temperature, realistic ambient temperature, and different vehicle speeds. The results showed that the novel MPC strategy saved energy consumption by 5.9%–10.3% in these cases when compared to the PI strategy, demonstrating the effectiveness and feasibility of the proposed MPC control. • Proposed a model predictive control strategy for thermal management systems in EVs. • Developed a control-oriented model based on data-driven methods and physical principles. • Minimizes total power consumption while maintaining cabin and battery temperatures. • Results demonstrate the effectiveness of the MPC strategy under various conditions. [ABSTRACT FROM AUTHOR]

As the national digitization process accelerates, the number and energy consumption of data centers are increasing year by year. Want to reduce the PUE of the data center. It is correct to start from the perspective of optimizing the energy consumption of air conditioning system operation. In this paper, a predictive optimization control method that is different from previous real-time optimization control is proposed. Predict, optimize, control and adjust the operating parameters of the air conditioning water system in the data center. At the same time, it solves the energy saving problem of the air conditioning system and the lag problem of previous adjustments. Predictive optimization control has broad prospects in research on operation optimization of data center air conditioning water systems. • Reducing air conditioning water system energy consumption in data centers can lower PUE. • Predictive optimization can solve the lag problem of water systems. • After global prediction optimization, energy consumption was saved by 12.9%. [ABSTRACT FROM AUTHOR]

• The vertical temperature gradient were analyzed in offices. • An evaluation index for air-conditioning energy consumption was proposed. • The energy-efficiency advice on air-conditioning for offices is given. Installing curtains on windows prevents direct sunlight from entering the indoor working space. It also alters the direction of solar heat radiation and the spatial distribution characteristics of indoor heat gain. This paper analyzes the relationship between the change of heat gain in the working and non-working spaces of the room under the effect of curtains. Meanwhile, it proposes an index of The Useless Cooling Capacity Ratio (UCCR) of the room to analyze its useless cooling capacity. In the hot summer and cold winter region, this study employs experimental and simulation methods to investigate the influence of solar radiation intensity, curtain thermal characteristics, and other factors on the vertical temperature gradient and heat flux indoor during the summer. The results showed that installing curtains on the windows reduced the vertical temperature gradient of the working space and improved the thermal comfort. The vertical temperature gradients in the working and non-working zones under the experimental conditions were 0.24 °C/m and 0.66 °C/m for the room with curtains and 0.62 °C/m and 0.57 °C/m for the room without curtains, respectively. Curtains can regulate the spatial distribution of heat loads and UCCR in a room and make the room more energy efficient. The UCCR for rooms with and without curtains were 24 % and 18 %, respectively, with curtains adding 120 kJ of useless cooling capacity to the work area. [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]

Today waste heat recovery of air conditioning is receiving considerable attention worldwide. This paper reports investigation on the development of a stand-alone thermoelectric power generator (STEG) using heat of refrigerant leaving the condenser and self-cooled by condensate of a split-type air conditioning. The STEG is composed of a small hollowed aluminum block integrated in the hot refrigerant line leaving the condenser to collect and transfer heat to the hot side of two thermoelectric (TE) modules 40 x 40 mm installed on the right and left. To cool the TE modules, water condensate circulates by gravity inside two aluminum alloy blocks. Tests were conducted using 2-tons split-type air conditioner installed in a 64 m3 room of a residential house in Bangkok. Electrical current generated by the two TE modules was recorded separately and connected in series three times for each case. Experimental results showed that STEG can produce electrical current continuously and varied depending on the operation on the compressor. The more important the cooling, the higher is the voltage produced. The maximum recorded voltage is about 200-250 mV by each TE module and doubled with the TE modules connected in series. The average water condensate flowrate was 11.56 g/min. The average coefficient of performance (COP) and the average Energy Efficiency Ratio (EER) of the air conditioning with STEG were 1.87-1.88 and 6.38-6.42 respectively, 3% higher than those of the conventional system. [ABSTRACT FROM AUTHOR]

The heat metering of heating is a main link in energy conservation of ventilating airconditioning (VAC) heating. It is of great practical significance to study the heat metering of heating. However, not many scholars have estimated the heat index based on building heating area, in the light of thermal comfort. Therefore, this paper estimates the heat index of VAC heating area considering thermal comfort. Firstly, we analyzed the various factors affecting human thermal comfort. Then, four environmental factors were selected, namely, indoor air temperature, relative air humidity, mean radiation temperature of envelope surface on human body, and indoor vertical air error. In addition, we selected suitable thermal comfort indices to predict and assess the thermal environment under indoor VAC heating. After that, we analyzed the indoor heat flow under VAC heating, examined the components of energy consumption of indoor VAC heating, and estimated the heat index of VAC heating area. Next, a mathematical model was constructed for heat metering, according to the heat consumption of the building VAC, and the composition of the heat consumption of occupants. Experimental results verify the effectiveness of the proposed thermal metering model. [ABSTRACT FROM AUTHOR]

Active chilled beams (ACBs), as indoor air terminal units in heating, ventilation, and air conditioning (HVAC) systems, have been of great concern in recent years due to the requirements of energy savings and indoor thermal comfort. This paper reviews the structure and application of ACB, which is helpful in identifying current research scenarios and prospects. The results indicate that the structure of ACBs will be improved based on the entrainment ratio (ER), cooling performance and acoustic behavior as the manufacturing level evolves. The mathematical model of ACBs is constantly being optimized, although there is still room for simplification to fit practical applications. Research on airflow patterns and thermal comfort can be extended to larger spaces (including buildings with large sensible heat ratios) and various climates. Based on the optimization of the ACB mathematical model, modern control theory can be focused on the research on ACB control system. ACB system has a coupling trend with other systems to achieve the comprehensive utilization of energy due to the characteristics of high-temperature cooling. In addition, numerous technical issues remain to be resolved to reduce unnecessary energy consumption. These conclusions also provide a simple reference for research and design in the area where this technology is still in its infancy. [ABSTRACT FROM AUTHOR]

Energy efficient control of thermal comfort has been already an important part of residential heating, ventilation, and air conditioning (HVAC) systems. However, the optimisation of energy saving control for thermal comfort is not an easy task due to the complex dynamics of HVAC systems, the dynamics of thermal comfort and the trade-off between energy saving and thermal comfort. To solve the above problem, we propose a deep reinforcement learning-based thermal comfort control method in multi-zone residential HVAC. In this paper, firstly we design a SVR-DNN model, consisting of Support Vector Regression and a Deep Neural Network to predict thermal comfort value. Then, we apply Deep Deterministic Policy Gradient (DDPG) based on the output of the SVR-DNN model to achieve an optimal HVAC thermal comfort control strategy. This method can minimise energy consumption while satisfying occupants' thermal comfort. The experimental results show that our method can improve thermal comfort prediction performance by 20.5% compared with DNN; compared with deep Q-network (DQN), energy consumption and thermal comfort violation can be reduced by 3.52% and 64.37% respectively. [ABSTRACT FROM AUTHOR]

Air-cooled chillers are extensively employed to meet cooling demand in commercial and tertiary sectors. To enhance their performance during part-load operation, variable-speed drives have been integrated into compressors and condenser fans. In existing systems, condenser fans are either operated at a fixed speed or modulated to maintain a predetermined condensing pressure or temperature difference between the refrigerant and outdoor air. In the search for increased efficiency, it is worth investigating innovative control strategies aimed at minimizing energy consumption. A preliminary mapping of chiller performance at different fan speeds, loads, and operating conditions is required to achieve this goal. In this respect, this paper investigates the operation of air-cooled chillers equipped with variable-speed condenser fans, both in the case of constant- and variable-speed compressors. An ad hoc matrix test is adopted to cover appropriate ranges conditions, in terms of load and outdoor air temperatures. Performance maps are developed for a 50-kW c chiller using a 1-D simulator. As the main findings, it may be stated that (i) in the case of the variable-speed chiller, the energy efficiency ratio increases almost linearly with fan speed, resulting in an 8.8% increase when increasing the speed from the nominal to the maximum; (ii) in the case of constant-speed chillers, the system exhibits a different behavior with the cooling capacity and the energy efficiency ratio increasing with the fan speed between 380 and 980 rpm and then decreasing (slightly or sharply, at full and part load respectively) with fan speed between 980 and 1280 rpm, and a percentage increase of EER in the range 7.8–45% is observed. Also, the sensitivity of such results to the system design is investigated, analyzing a chiller equipped with a larger condenser that resulted in achieving minimum energy consumption at an optimal 980 rpm fan speed. Finally, for a constant-speed chiller serving an office building in the Mediterranean, the proposed fan control strategy could yield an electricity saving of up to 12.1% compared to the base case, confirming the potential for energy savings through optimized and system-tailored management of condenser fan speed. • The effect of variable-speed fans on the performance of air-cooled chillers is investigated. • Maps are developed for chillers with both variable-speed and constant-speed compressors. • Fan speed significantly influences the cooling capacity of constant-speed chillers. • In variable-speed chillers, it is advisable to always run the condenser fan at high speed. • The enhanced fan strategy enables 12.1% energy savings for a chiller in an office building. [ABSTRACT FROM AUTHOR]