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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]

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]

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]

Fuel consumption optimization is a critical field of research within the automotive industry to meet consumer expectations and regulatory requirements. A reduction in fuel consumption can be achieved by reducing the energy consumed by the vehicle. Several subsystems contribute to the overall energy consumption of the vehicle, including the air conditioning (A/C) system. The loads within the A/C system are mainly contributed by the compressor, condenser fan, and underhood aerodynamic drag, which are the components targeted for overall vehicle energy use reduction in this paper. This paper explores a new avenue for A/C system control by considering the power consumption due to vehicle drag (regulated by the condenser fan and active grille shutters (AGS)) to reduce the energy consumption of the A/C system and improve the overall vehicle fuel economy. The control approach used in this paper is model predictive control (MPC). The controller is designed in Simulink, where the compressor clutch signal, condenser fan speed, and AGS open-fraction are inputs. The controller is connected to a high-fidelity vehicle model in Gamma Technologies (GT)-Suite (which is treated as the real physical vehicle) to form a software-in-the-loop simulation environment, where the controller sends actuator inputs to GT-Suite and the vehicle response is sent back to the controller in Simulink. Quadratic programming is used to solve the MPC optimization problem and determine the optimal input trajectory at each time step. The results indicate that using MPC to control the compressor clutch, condenser fan, and AGS can provide a 37.6% reduction in the overall A/C system energy consumption and a 32.7% reduction in the error for the air temperature reference tracking compared to the conventional baseline proportional integral derivative control present in the GT-Suite model. [ABSTRACT FROM AUTHOR]

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 energy transition requires optimal knowledge of the thermal behaviour of different passive strategies. This paper explores the impact of 28 variables representing 4 shading devices, 5 external wall compositions (Uw), 3 window types (Uw), 4 window-to-wall ratios (WWR), 4 types of climates represented by 4 cities, and 8 orientations. Applying the Latin hypercube sampling method, a campaign of 300 dynamic thermal simulations is performed to assess the impact of the variables selected using the weighted generalised linear regression method for the energy demand for air conditioning, the energy demand for lighting, and the environmental impact expressed in kg of CO2. The model of energy demand for cooling (R² = 0.951) shows that the weather data is the variable that most explains energy demand, followed by the glazing ratio, the thermal characteristics of external walls, and shading devices. The model explaining the energy demand for lighting (R² = 0.945) shows that the WWR and shading devices, the weather data, and the orientation, influence the energy demand for lighting. Finally, the model explaining the embodied carbon footprint (kg of CO2) (R² = 0.989) shows that external walls and window type are the main influencing factors. Finally, the best combination for balancing the cooling-lighting-embodied carbon balance equation is discussed. [ABSTRACT FROM AUTHOR]

With the advent of flexible working arrangements, we are observing a dramatic shift in how buildings are occupied today, which presents an opportunity to optimize Heating, Ventilation, and Air Conditioning system temperature setpoints based on variations in occupancy. Guidelines often suggest the adoption of the highest or lowest setpoint or setback to minimize energy consumption in hot or cold climates, respectively. However, at outdoor temperatures where variations in occupancy heat loads prompt buildings to fluctuate across cooling, free-running, and heating mode, optimal setpoints and setbacks are not always the lowest or highest. In addition, the perturbations caused by rapid switching between setpoint and setback could diminish energy savings due to system destabilization. This paper aims to systematically compare the potential energy savings from fixed and optimal setpoints and setbacks across wide-ranging occupancy scenarios (four occupancy rates and 14 patterns). Energy simulations were conducted using the Department of Energy reference models for small, medium, and large office buildings to enable an exhaustive search of optimal setpoint/setbacks in 17 climate zones. Explored setpoints were 19.5 °C to 25.5 °C with intervals of 1 °C, and setbacks were 17 °C/19 °C for heating and 26 °C/28 °C for cooling. The findings indicate that, on average, while lower occupancy heat loads results in 5.48% energy reduction, a conventional fixed setpoint and setback strategy provides an additional 11.80%, and optimal selection of setpoints and setbacks could provide an additional 34.36–38.08%, emphasizing the untapped potential energy saving. To facilitate practical applications, this paper presents an interactive graphical interface: Optimal Temperature Setpoint Tool. [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]

In the context of the "Dual-Carbon Strategy", the seamless integration and optimal utilization of renewable energy sources present a pressing challenge for the emerging power system. The advent of demand-side response technology offers a promising solution to this challenge. This study proposes a two-stage response control strategy for multiple DR clusters based on the specific response time characteristics of industrial and residential loads. The strategy enhances the utilization rate of wind power, harnesses the joint response capability of various types of loads on the demand side, and ensures the overall revenue of the load aggregator (LA). It underscores the importance of industrial loads in large-scale energy consumption control throughout the overall consumption response process, while residential load clusters exhibit quick response flexibility. A homogeneous energy consumption sorting unit response strategy is established from the perspective of a residential load variable-frequency air conditioning cluster unit. This strategy addresses the challenge faced by industrial electrolytic aluminum plants in coping with long-term response intervals amidst significant fluctuations in wind power consumption demand, which may lead to incomplete consumption. This study constructs a response model based on industrial and residential time-sharing tariffs, as well as the aggregator consumption penalty price, with the optimal load energy economy index serving as the evaluation criterion. A series of simulations are conducted to comprehensively evaluate the energy consumption of the two load clusters at all times and the total revenue of the aggregator in the response zone. The objective is to achieve a win–win situation for the total wind power energy consumption rate and the aggregator's economy. The results of the simulations demonstrate that the response control strategy proposed in this study enhances the overall energy consumption rate by nearly 4 percentage points compared to a single industrial cluster. The total benefit of the load aggregator can reach CNY 941,732.09. The consumption response scheduling strategy put forward in this paper bolsters wind power consumption, triggers demand response, and significantly propels the comprehensive construction and development of the dual-high power grid. [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]

This paper expands upon the state of the art in nonlinear modeling of automotive air conditioning systems. Prior models considered only the effects of the refrigerant compressor and the condenser fan. There are two new aspects included here. First, we create a mathematical model for front-end underhood airflow, considering vehicle speed, condenser fan rotational speed, and active grille shutter position. In addition, we present a new model for the power consumption of the vehicle associated with aerodynamic drag caused by underhood flow, as well as a fan power model which accounts not only for changes in rotational speed but also changes in flow rate. The models developed in this paper are coded in MATLAB/Simulink and assessed for various vehicle driving conditions against a higher-fidelity vehicle energy management model, showing good agreement. By including the active grille shutters as a controllable actuator and the impact of underhood flow on vehicle drag and fan power consumption, control schemes can be developed to holistically target reduced energy consumption for the air conditioning system and, thus, improve the overall vehicle energy efficiency. [ABSTRACT FROM AUTHOR]

Control strategies for variable-air-volume (VAV) air conditioning significantly affect both the air quality within buildings and the consumption of building energy. Current control techniques effectively regulate room temperature using feedback on temperature discrepancies, yet they also elevate the wear on terminal devices and boost the energy usage of the supply fan. In this paper, the hysteresis and inertia parameters of end air valves and supply fans under two seasonal conditions are derived from experimental data. Aiming at the problems of frequent switching of the end air valve, long total switching stroke, and high energy consumption of the air supply fan, a fuzzy PI regulation method is proposed based on the original pressure-independent series PI regulation, which effectively solves the above problems. Initially, data on how room temperature reacts to changes in air supply fan speed and the position of end air valves during winter and summer were gathered. Following model identification, parameters for various seasonal conditions were determined. Secondly, the roles of different components in the variable-air-volume regulation process were investigated. Investigations revealed that within pressure-independent variable-air-volume control, the supply fan and end air valve emerged as the primary subjects of the study. A double-closed-loop control with the speed control of the supply fan as the outer loop and the opening control of the end air valve as the inner loop was adopted. Compared with the traditional serial PI regulation, the room temperature error of this method was increased, but it reduced the total stroke of the valve by more than 43%, which greatly reduced the valve's loss and noise and saved more than 2.7% of the energy consumption of the air supply fan. [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]

The Heating, Ventilation, and Air Conditioning (HVAC) control system is in charge of the building's energy efficiency. Indoor energy consumption trends can be intelligently monitored and minimized. Occupancy data is essential for saving a significant amount of energy. This energy footprint can play an important part in modern smart buildings to improve indoor green environments while lowering costs. Traditional energy monitoring and control systems can be enhanced by installing an occupancy monitoring system, which consists of a network of sensors and cameras. In this paper, we offer a novel and innovative Convolutional Neural Network (CNN) based on real-time camera occupancy detection and recognition algorithms across various types of sensors, which provides realistic low-cost energy-saving solutions with robust Graphical Processing Units (GPUs). Decisionmaking tools can be used to select the appropriate occupancy detection and recognition alternative for indoor environment and energy monitoring and management. In this research work, we develop the\Fermatean Fuzzy Prioritized Weighted Average (FFPWA) operator and Fermatean Fuzzy Prioritized Weighted Geometric (FFPWG) operator". In the end, we give an algorithm for an Intelligent Decision Support System (IDSS) using proposed Aggregation Operators (AOs) to compare our CNN based method with other existing sensors techniques. [ABSTRACT FROM AUTHOR]

An air-conditioning system (ACS), which consumes large amounts of high-grade energy, is essential for maintaining the indoor thermal environment of modern buildings. However, an ACS consumes almost half of the total energy of the building. Therefore, it is necessary to reduce the energy consumption of the ACS to promote energy conservation and emission reduction in the building sector. In fact, there is an abundance of waste heat and low-grade energies with the potential to be utilized in ACS in nature, but many of them are not utilized efficiently or cannot be utilized at all due to the low efficiency of thermal energy conversion. Known as a passive thermal transfer device, the application of a heat pipe (HP) in the ACS has shown explosive growth in recent years. HPs have been demonstrated to be an effective method for reducing building cooling and heating demands and energy consumption in ACS with experimental and simulation methods. This paper summarizes the different HP types applied in the ACS and provides brief insight into the performance enhancement of the ACS integrated with HP. Four types of HPs, namely tubular HP (THP), loop HP (LHP), pulsating HP (PHP) and flat HP (FHP), are presented. Their working principles and scope of applications are reviewed. Then, HPs used in natural cooling system, split air conditioner (SAC), centralized ACS (CACS) and cooling terminal devices are comprehensively reviewed. Finally, the heat transfer characteristics and energy savings of the above systems are critically analyzed. The results show that the performance of the HP is greatly affected by its own structure, working fluid and external environmental conditions. The energy saving of ACS coupled with HP is 3–40.9%. The payback period of this system ranges from 1.9–10 years. It demonstrates that the HP plays a significant role in reducing ACS energy consumption and improving indoor thermal comfort. [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 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]

In electric buses, heating, ventilation and air conditioning are responsible for up to 50% of the energy consumption. It is therefore necessary to identify improved thermal settings to minimize the energy consumption, while guaranteeing good thermal comfort. Hence, an accurate prediction of the passengers' thermal sensation (TS) is needed. One of the most widely used models for TS prediction is the PMV-PPD model, which has been shown to provide reliable results in uniform, steady-state climatic conditions. Since these are not present in an urban bus, the accuracy of the PMV-PPD model diminishes. Additionally, some of the parameters needed are difficult to obtain (i.e., clothing insulation). This paper presents seven different machine learning models (ML) for the prediction of TS using three different sets of parameters. The first set comprises five parameters similar to the PMV-PPD model, the second uses only two, and the third uses all parameters available. To obtain the necessary data, climatic measurements in an electric bus in Berlin, Germany, were made. These measurements were performed in summer for ambient temperatures between 14.7 °C and 32.0 °C. Person-related information as well as the thermal comfort assessment were obtained via surveys. Despite the relatively small data set, four of our seven ML models performed well with a median accuracy between 70.3% and 69.4%. This could also be observed when using only two parameters. Hence, the efforts to gain experimental data can be reduced significantly. For the PMV-PPD model, a median shift of +1 was observed for mild and warm TS. The median accuracy rises from 48.8% without shift to 68.8% with shift. [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]

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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]

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]

Coefficient of performance of air conditioning system is the ratio of cooling capacity and power consumption, and also is an important parameter to characterize the performance of the system. Based on an actual ground source heat pump system in Wuhan, China, this paper calculated the coefficient of performance of the system under long-term continuous cooling operation and then evaluated the performance of the system through experiment. The continuous testing time ranged from June 1, 2017, to June 15, 2017, and the outside air temperature ranged from 21 °C to 32 °C during the testing progress. The experimental results show that the coefficient of performance of the system changes non-linearly with time, and varies greatly with the atmospheric temperature. The results also show that the coefficient of performances average value of the system is about 3.3, and the system has good performance under refrigeration conditions. This paper helps to understand the changes in the distribution characteristics of the coefficient of performance of the ground source heat pump system and promote the wide application of the ground source heat pump system. [ABSTRACT FROM AUTHOR]

High energy consumption is an increasingly serious problem for air conditioners in mobile communication base stations, and a composite system of the air conditioning and the heat pipe is proposed to solve the problem. The experimental data obtained in Zhengzhou City elucidated the high efficiency in energy saving. The system offers a new opportunity for wide applications in outdoor communication cabinets. [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]

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]

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]

Energy efficiency improvement is often hindered by the energy efficiency gap. This paper examines the effect of short-run temperature fluctuations on the Energy Star air conditioner purchases in the United States from 2006 to 2019 using transaction-level data. Results show that the probability of purchasing an Energy Star air conditioner increases as the weekly temperature before the transaction deviates from 20–22 °C. A larger response is related to fewer cooling degree days in the previous years, higher electricity prices/income/educational levels/age/rate of owners, more common use of electricity, and stronger concern about climate change. 1 °C increase and decrease from 21 °C would lead to a reduction of total energy expenditure by 35.46 and 17.73 million dollars nationwide (0.13% and 0.06% of the annual total energy expenditure on air conditioning), respectively. Our findings have important policy implications for demand-end interventions to incorporate the potential impact of the ambient physical environment. The probability of purchasing an energy-efficient air conditioner increases as the temperature deviates from 20–22 °C in the United States, with the response varying by electricity price, background climate, and demographic characteristics. [ABSTRACT FROM AUTHOR]

An innovative tri-generation system powered by solar energy for water desalination, air-conditioning, and electrical power production is proposed and investigated numerically in this paper. The system is designed for small and medium-sized buildings in countries that are rich in solar energy but poor in fossil fuels and water resources. The devised system includes a solar system (evacuated tube collectors and thermal energy storage unit), an Organic Rankine Cycle (ORC), a Humidification and Dehumidification (HDH) water desalination system, and a Desiccant Cooling System (DCS). A detailed parametric study of the developed system is carried out for a wide range of operating conditions and design parameters on the system's productivity and performance parameters. It is found that: (i) The proposed tri-generation system can deliver high electrical power, fresh water, space cooling capacity, and Energy Utilization Factor (EUF) of 104.5kW, 72.37kg/h, 25.48kW, and 0.2643 respectively. In comparison to the basic system, the EUFimp and ASC, sav parameters were enhanced having maximum values of 69.9% and 41.14% respectively. General numerical correlations derived from the numerical data can predict the system productivity and performance parameters within reasonable error. [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]

Since daily energy needs are increasing, it is imperative to find ways to save energy, such as improving the energy consumption of buildings. Heating Ventilating and Air- Conditioning (HVAC) loads account for the majority of a building's energy use. The accurate estimation of energy consumption and the examination of various ways to improve the energy efficiency of buildings are very important. This paper presents an analysis of HVAC loads in a residential building by examining three Neural Networks (NNs): Feed-Forward (FF), Cascaded Forward Backpropagation (CFBP), and Elman Backpropagation (EBP) networks, based on Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Relative Error (MRE). Furthermore, these networks were combined in hybrid NNs to obtain more optimized results. These results were also compared with other approaches and showed better prediction performance. [ABSTRACT FROM AUTHOR]

This paper uses an interview study in Doha, the capital city of Qatar, to consider the role of qualitative methods in identifying how human relationships with air-conditioning are influenced. Drawing on discussions with three groups of comparatively affluent residents who spent significant amounts of time in air-conditioned spaces, it makes four points to researchers hoping to encourage less energy demanding lifestyles. The first is that 'comfort is only one aspect' when a premature focus on comfort obscures other potentially important analytical concerns. The second is that 'air-conditioning changes cultures' when valuable insights may be gleaned from what people say about how it creates, rather than responds to, local preferences and expectations. The third is that 'comparison can be instructive' when different groups may feel themselves to possess very different relationships with air-conditioning. The fourth is that 'discussion can disappear' when those hoping to influence these relationships may benefit from understanding when and how 'conditioning conversations' currently occur and how they could be part of processes of positive local change. After substantiating these points, wider conclusions are drawn about the promotion of less energy consumptive ways of adapting to a warming world and the value of this approach to the cultural geography of air-conditioning. [ABSTRACT FROM AUTHOR]

A double-skin façade (DSF) is a multilayered skin that was initially designed for the cold climate of European countries with noticeable success. Since then, countries in different climates started looking into the possibility of adopting DSF through the use of computer simulations to avoid any unforeseen problems in real life. This study aims to look into the possibility and level of success in using DSF in the Middle East's hot-arid climate, making it a challenge compared to European countries. The study utilized a quantitative investigative approach in analyzing the results of some studies done in different countries in the Middle East. After looking into the results from different papers, careful considerations have to be made for the building due to its location and microclimate to determine specific parameters (e.g., combination of transparent and opaque materials, proportion between floor level and screen height, orientation and cavity depth), these would lead to a significant impact on reducing a building's cooling loads and energy efficiency. It is worth noting that mechanical ventilation (e.g., supplying all return cool air from internal spaces into the DSF cavity or integrating the building's heating, ventilation, and air-conditioning system with the façade) is necessary for DSF in hot climates to cool and maintain its cavity's temperature to function properly. However, this increases cooling loads, energy consumption, and running costs of the building which architects have to consider to determine the most efficient and economical solution in material and equipment. [ABSTRACT FROM AUTHOR]

Energy is an efficient source in the present world and it is very significant. Huge building systems such as luxury hotels, commercial complex, malls and educational institutes require chiller plants for air-conditioning purpose in their premises. In this paper, a case analysis on energy savings and efficiency improvement of a chiller plant is done for a luxury hotel building located at Chennai, Tamil Nadu, India. The building consists of carrier make 2 Nos. 633 kW/180 TR and 1 No. 1185 kW/337 TR water cooled chillers. The chiller plant system at the site consumes up to 43% of the facility's annual energy cost. To increase the energy savings and efficiency of existing system and to achieve a lower number on the Specific Power Consumption (SPC), the existing chiller plant is retrofitted with 1 No. 879 kW/250 TR Premium efficiency chiller and an energy analysis is done by simulating the proposed chiller using Chiller Plant Automation (CPA) software and by understanding the energy usage of the chiller as per the site operating load and weather conditions. With the change in chiller, the system efficiency of the hotel increased by 40% and the energy consumption reduced to 38%. [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]

Past investigations have identified phase change emulsions (PCMEs) as potential working fluids that could be used to reduce energy consumption in heating, ventilation and air conditioning (HVAC) systems. But how PCME behaves in a fin-and-tube heat exchanger, which is commonly found in HVAC system, is still unclear. The paper focused on theoretical studies of thermal performance of a novel PCME PCE-10 in a fin-and-tube heat exchanger. The research analyzed heat transfer and flow behavior in fin-and-tube heat exchangers and the results showed that PCE-10 has several performance advantages as a cold storage medium, which provides a basic basis for the design of a new type of practical cold storage device. [ABSTRACT FROM AUTHOR]

Cooling and air-conditioning systems are responsible for the highest energy consumption in buildings located in hot areas. This high share does not only increase the building energy demand cost but also increases the environmental impact, the topmost awareness of the modern era. The development of traditional systems and reliance on renewable technologies have increased drastically in the last century but still lacks economic concerns. Passive cooling strategies have been introduced as a successful option to mitigate the energy demand and improve energy conservation in buildings. This paper shed light on some passive strategies that could be applied to minimise building cooling loads to encourage the movement towards healthier and more energy-efficient buildings. For this purpose, seven popular passive technologies have been discussed shortly: multi-panned windows, shading devices, insulations, green roofing, phase change materials, reflective coatings, and natural ventilation using the windcatcher technique. The analysis of each strategy has shown that the building energy could be improved remarkably. Furthermore, adopting more passive strategies can significantly enhance the building thermal comfort even under severe weather conditions. [ABSTRACT FROM AUTHOR]

The integration between heat pumps and renewable energy sources is a recognized strategy to be pursued to reduce primary energy consumption in new or energetically retrained residential buildings. This paper presents the experimental research of a novel integrated system for air conditioning developed by RSE and built in the Terni laboratory at the Business Innovation Centre Umbria. The system named SINCLER (Sistema Integrato di Climatizzazione Efficiente e Rinnovabile) has been designed to meet the needs of air conditioning and domestic hot water for civil buildings using a reversible air heat pump assisted by hybrid solar panels. The thermal energy produced by the hybrid panels is used to produce hot water, while the electrical energy to power the heat pump and the various electrical equipment. The system also relies on two different storage tanks, one inside the heat pump and the other directly connected to the solar circuit, allowing a better temperatures management of the hybrid panels which effectively improved their efficiencies. The performance of the system has been evaluated in operation from June to December 2016, registering a seasonal EER (from June to September) equal to 2.9 and a seasonal COP (from October to December) equal to 3.2. [ABSTRACT FROM AUTHOR]

For areas with hot summer and cold winter, air conditioning is an essential tool to improve the living environment, but the traditional air conditioning needs to consume a lot of energy in cooling and heating, the fan operation noise is large and the sense of blowing will be uncomfortable. This paper briefly introduced the basic structure of a ground-source heat pump-floor radiant air conditioning system and the control strategy which was used for regulating the operation of the air conditioning system. Then, in order to ensure the appropriate comfort and reduce energy consumption, the control strategy was optimized. An experimental room in Xining, Qinghai province, was taken as an example for analysis. The results showed that the thermal comfort under the control strategy proposed in this study was more stable at the moderate degree and the temperature was slightly higher under the traditional control strategy, although the indoor temperature fluctuation under the optimal control strategy was large and the temperature was slightly lower than that under the traditional control strategy; under the control strategy proposed in this study, the air conditioning system had shorter operation time and less power consumption. [ABSTRACT FROM AUTHOR]