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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]

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]

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]

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]

Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings' energy is mostly used for heating and/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review. [ABSTRACT FROM AUTHOR]

The conventional multi-zone HVAC (Heating Ventilation and Air Conditioning) system includes a lot of Variable Air Volume (VAV) boxes to regulate the corresponding zone temperature, and centralized equipment (air supply fans and ducts, etc.) to supply conditioned air in the air-side. With the limitation of the total supply air mass rate in centralized equipment and the priority of zones' requirement, it becomes a question of resource allocation and schedule on how to coordinate air mass rate to each zone. It is also intricate to control each zone's temperature in a fully distributed fashion due to this global constraint. In this paper, a distributed model predictive control (DMPC) with priority coordination is proposed for multi-zone HVAC systems. This strategy is composed of a priority coordinated layer and a lower DMPC layer. In the coordinated layer, a thermal bound calculation mechanism with priority is presented to adjust thermal bounds for DMPC to resolve the resource allocation problem when the subsystems are competing for the resource. In DMPC, an operational cost-saving controller is put forward, and the global resource limit is guaranteed by transforming the optimization problem into a consensus problem, which is running in a fully distributed way. In this control scheme, the original infeasible DMPC problem is relaxed, and the entire system is functioning well with specified performance compromise. The effectiveness of the proposed scheme is proved by simulation examples. • The modified DMPC is used for HVAC to reduce cost while satisfying thermal comfort. • The DMPC is converted into a consistency problem and solved in a fully distributed way. • The multi-priority thermal bound calculation is used to resolve resource competing. • The original MPC problem is functioning well with specified performance compromise. [ABSTRACT FROM AUTHOR]

The control of HVAC (Heating Ventilation and Air Conditioning) systems is usually complex because its modeling in certain cases is difficult, since these systems have a large number of components. Heat exchangers, chillers, valves, sensors, and actuators, increase the non-linear characteristics of the complete model, so it is necessary to propose new control strategies that can handle the uncertainty generated by all these elements working together. On the other hand, artificial intelligence is a powerful tool that allows improving the performance of control systems with inexact models and uncertainties. This paper presents new control alternatives for HVAC systems based on LAMDA (Learning Algorithm for Multivariable Data Analysis). This algorithm has been used in the field of machine learning, however, we have taken advantage of its learning characteristics to propose different types of intelligent controllers to improve the performance of the overall control system in tasks of regulation and reference change. In order to perform a comparative analysis in the context of HVAC systems, conventional methods such as PID and Fuzzy-PID are compared with LAMDA-PID, LAMDA-Sliding Mode Control based on Z-numbers (ZLSMC), and Adaptive LAMDA. Specifically, two HVAC systems are implemented by simulations to evaluate the proposals: an MIMO (Multiple-input Multiple-output) HVAC system and an HVAC system with dead time, which are used to compare the results qualitatively and quantitatively. The results show that ZLSMC is the most robust controller, which efficiently controls HVAC systems in cases of reference changes and the presence of disturbances. • New control alternatives for HVAC systems based on fuzzy approaches. • Learning Algorithm for Multivariable Data Analysis (LAMDA) in the definition of intelligent controllers. • MIMO (Multiple-input Multiple-output) HVAC and HVAC with dead time controlled by LAMDA approaches. • Utilization of LAMDA approaches for fuzzy modelling and control of HVAC systems. [ABSTRACT FROM AUTHOR]

Model predictive control is widely used as a control technology for the computation of optimal control inputs of building heating, ventilating, and air conditioning (HVAC) systems. However, both the benefits and widespread adoption of model predictive control (MPC) are hindered by the effort of model creation, calibration, and accuracy of the predictions. In this paper, we apply the data-enabled predictive control (DeePC) algorithm for designing controls for building HVAC systems. The algorithm solely depends on input/output data from the system to predict future state trajectories without the need for system identification. The algorithm relies on the idea that a vector space of all input-output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given the input signal is persistently exciting. Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated building modeled in EnergyPlus is a modified commercial large office prototype building served by an air handling unit-variable air volume HVAC system. Temperature setpoints of zones are used as control variables to minimize the HVAC energy cost of the building considering a time-of-use electricity rate structure. Furthermore, sensitivity analysis is conducted to gain insights into the effect of parameter tuning on DeePC performance. Simulation results are used to illustrate the performance of the algorithm and compare the algorithm with model-based MPC and occupancy-based setpoint controller. Overall, DeePC achieves similar performance compared to MPC for lower engineering effort. [ABSTRACT FROM AUTHOR]

Heating, ventilation, air conditioning (HVAC) is one of crucial system in a vehicle. Unfortunately, its performance can be affected by the vibration of HVAC components, which subsequently produced unwanted noises. This paper presents an innovative design solution which called as tuneable dynamic vibration absorber (TDVA) to reduce the humming-type noise and vibration in the HVAC system. A detail investigation is carried by developing a lab-scale HVAC model that has the capability to imitate the real HVAC operation in a vehicle. An alternated air-condition (AC) with a fixed blower speed is implied in the study. The analysis of hummingtype noise and vibration induced from the HVAC components are performed, and the TDVA is designed and tuned according to the natural frequency of the AC pipe before the attachment. The humming-type noise and vibration characteristics of the HVAC components are compared before and after the implementation of the TDVA. The findings shown that the HVAC model data compares well with the vehicle data, whereby the implementation of TDVA is found to reduce the vibration of the AC pipe by 79% and 61% in both idle and operating conditions and this subsequently improved the humming-type noise of the HVAC system. It also been observed that the TDVA has an effective frequency range around 75-255 Hz and 100-500 Hz for the HVAC model and vehicle systems, respectively. [ABSTRACT FROM AUTHOR]

The evaluation and optimization of existing operation strategies are significant for energy-saving in heating, ventilation, and air conditioning (HVAC) systems. The personalized characteristics of HVAC systems, however, pose a challenge in developing the baseline. This paper proposes an unsupervised data mining-based framework for the evaluation and optimization of HVAC system operation strategies. The framework involves several steps: firstly, data preprocessing is performed to clean and reduce the raw data. Then, an improved clustering method is used to divide the external conditions of system operation, followed by an association rule algorithm to mine the evaluation baseline for operation strategies. Finally, the low-energy efficiency operating strategies can be identified and optimized using the baseline. Validity of the proposed framework is confirmed by practical cases and data. The results indicate that the mined operation evaluation baseline effectively identifies low-energy efficiency operating strategies that can be improved, resulting in energy savings of 6.9 %. Notably, optimizing the operation number of chillers and water pumps has the best energy-saving potential. Furthermore, compared to model-based operation evaluation methods, this data-driven approach offers advantages such as low computational costs and quick execution. • An unsupervised data mining-based framework is proposed for strategy identification. • Improved clustering method is used to divide external operation conditions. • Evaluation baseline of operation strategy is generated with the proposed method. • Actual HVAC system is used to verify the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, a coordinated strategy of distributed model predictive control (DMPC) is proposed to accommodate Air Handling Unit (AHU) and Variable Air Volume boxes (VAV boxes) in a multi-zone HVAC (Heating Ventilation and Air Conditioning) system. A modified distributed model predictive control with equivalent local cooling cost and a total-air-mass-rate penalty term is implemented to local VAV boxes to regulate indoor temperature. The penalty term and AHU supply air temperature are updated in the upper coordinated layer to guarantee the total air mass rate within the limit and economic operation. In this framework, with the help of coordination from both DMPC and the upper layer, the operational cost from the HVAC system can be reduced. The effectiveness of the proposed scheme is proved by a numerical simulation. • A coordinated DMPC is proposed to accommodate equipment in multi-zone HVAC system. • A modified DMPC is implemented to regulate indoor temperature. • The coordinated layer is used to keep the total air mass rate within the limit. • The supply air temperature PID generates a suitable value for subsystems. [ABSTRACT FROM AUTHOR]

The decision making of a suitable heating, ventilating and air conditioning system's set-point temperature is an energy and environmental challenge in our society. In the present paper, a general framework to define such temperature based on a dynamic adaptive comfort algorithm is proposed. Due to the fact that the thermal comfort of the occupants of a building has different ranges of acceptability, this method is applied to learn such comfort temperature with respect to the running mean temperature and therefore to decide the suitable range of indoor temperature. It is demonstrated that this solution allows to dynamically build an adaptive comfort algorithm, an algorithm based on the human being's thermal adaptability, without applying the traditional theory. The proposed methodology based on the K-Nearest-Neighbour algorithm was tested and compared with data from an experimental thermal comfort field study carried out in a mixed mode building in the south-western area of Spain and with the Support Vector Machine method. The results show that K-Nearest-Neighbour algorithm represents the pattern of thermal comfort data better than the traditional solution and that it is a suitable method to learn the thermal comfort area of a building and to define the set-point temperature for a heating, ventilating and air-conditioning system. [ABSTRACT FROM AUTHOR]

Accurate indoor air temperature (IAT) predictions for heating, ventilation, and air conditioning (HVAC) systems are challenging, especially for multi-zone building and for different HVAC system types. Moreover, the nonlinearity of the buildings thermal dynamics makes the IAT prediction more difficult since it is affected by complex factors such as controlled and uncontrolled points, outside weather conditions and occupancy schedule. This paper presents a long short-term memory (LSTM) model to predict IAT for multi-zone building based on direct multi-step prediction with sequence-to-sequence approach. Two strategies, LSTM-MISO and LSTM-MIMO, are built for multi-input single-output and multi-input multi-output, respectively. The performance of these two strategies has been evaluated based on two case studies on real smart buildings using variable air volume (VAV) and constant air volume (CAV) systems. For both buildings, experimental results showed that the LSTM models outperform multilayer perceptron models by reducing the prediction error by 50%. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for optimal management of aggregated power demand of a group of heating, ventilating and air-conditioning (HVAC) units. The algorithm provides an advanced direct load control mechanism for HVACs that leverages the availability of smart thermostats, which are remotely programmable and controllable. The paper provides a theoretical basis and an optimal solution to the problem of cycling a large number of HVAC units while respecting customer-chosen temperature limits for the purpose of maximum load reduction. The problem is presented in a new light by transforming it into a job scheduling problem and is solved using a combination of a novel greedy algorithm and a binary search algorithm. By leveraging widespread availability of smart internet-based (also referred to as IoT-based) thermostats in today’s environment, the proposed approach can be readily applied to residential buildings without additional electrical/IT infrastructure changes. [ABSTRACT FROM AUTHOR]

High‐efficiency filtration in residential forced‐air heating, ventilation, and air conditioning (HVAC) systems protects equipment and can reduce exposure to particulate matter. Laboratory tests provide a measure of the nominal efficiency, but they may not accurately reflect the in situ efficiency of the filters because of variations in system conditions and changes in filter performance over time. The primary focus of this paper is to evaluate the effective filtration efficiency, which is inclusive of any loading and system impacts, in 21 occupied residential homes through in‐duct concentration measurements. We considered the role of filter media by testing both electret and non‐electret media, as well as the role of loading by considering new and used filters. The results show that filters with higher nominal efficiency generally had higher effective filtration efficiency in the same home. In terms of performance change, there is no significant difference in efficiency between initial and 3‐month non‐electret filters, but the efficiency of electret filters generally decreased over time. However, both nominal efficiency and performance change were vastly overshadowed by the wide variety in loading and system conditions across homes, making it hard to predict filter efficiency in a given home without in situ measurements. [ABSTRACT FROM AUTHOR]

The vast majority of fault detection and diagnosis (FDD) methods for air conditioning systems are developed for packaged commercial systems. This paper presents a method for obtaining key operating parameters for air conditioning systems (airflow rate, cooling capacity, system efficiency, and refrigerant mass flow) in a way that is well-suited for the residential sector. The method relies on an air-side capacity estimate and will be compared and contrasted with a more traditional method that relies on a refrigerant-side capacity estimate. These methods are compared in terms of their sensitivity to input parameters, their uncertainty in the outputs, and their sensor requirements. The proposed air-side sensing method requires fewer sensors and has a significant advantage for residential split systems because it requires no outdoor unit sensors. The air-side sensing method is then successfully implemented on a field operating residential system. The experimental results show that the proposed method is sensitive to manually introduced airflow faults. [ABSTRACT FROM AUTHOR]

In this paper, PSO-OMRC model is proposed for heating ventilation and air conditioning (HVAC) system. The PSO-OMRC predicts SPT in HVAC for energy saving and thermal comfort of persons in room. PSO-OMRC used data collected from temperature, humidity, PIR, and thermal camera sensors in HVAC installed rooms for SPT prediction. In existing HVAC systems, SPT is constant and consumes more energy. Moreover, SPT prediction using traditional machine learning algorithms considers various parameters such as room temperature, humidity and person count in room. Existing algorithms considers the above parameters for energy saving in HVAC and thermal comfort for persons, whereas other parameters such as room walls and ceiling temperature, human body temperature need to be considered for SPT prediction and for energy saving in HVAC. The PSO-OMRC model predicts the SPT based on number of occupants and microclimatic conditions such as environment temperature, humidity, room wall temperature and ceiling temperature. From experimental results, the utilization of the thermal image occupant model provides significant energy savings in the PSO-OMRC model. In nano coated room, PSO-OMRC model is evaluated with the implementation of a thermal image occupant model to improve energy savings, resulting in approximately 25.2% of energy savings while maintaining the level of thermal comfort. • Microclimatic and occupant data-based setpoint temperature prediction in HVAC. • Gaussian Process Regression algorithm is used to predict setpoint temperature. • The proposed PSO-OMRC model provides thermal comfort for occupants. • Nano coated room has considerable energy savings with PSO-OMRC model in HVAC. [ABSTRACT FROM AUTHOR]

Highlights • HVAC system, the objective function, limit conditions in the matrix or vector form. • A universal set of constant parameters, decision variables and limiting conditions. • System analysis and matrix calculation in the optimization of the HVAC system. • A global form of the objective function that captures all HVAC subsystems. Abstract The new complex selection strategy of low energy ventilating or air conditioning system (HVAC system) from a holistic perspective is presented in the paper. The strategy carries out the solution of the whole optimization problem that consists of the HVAC system model, optimization model and optimization procedure. The practical function is the starting point of the optimization problem, whereas the energy optimal variant of the HVAC system is the final stage. Universal sets of constant parameters, decision variables and limiting conditions of the HVAC system were formulated for the mathematical description of the problem. In optimization procedure, the sets of all variants and permissible variants of the HVAC system were defined based on the system analysis with the use of matrix calculation. The objective function was determined by the use of the simulation model of the HVAC system where the algorithms of energy optimal control is determined by dynamic optimization. [ABSTRACT FROM AUTHOR]

Abstract Taking advantage of the thermal inertia of a building to shift the cooling load during a demand response (DR) period is an important strategy for commercial buildings. However, researchers have consistently ignored the effects of the thermal inertia of air-conditioning systems on the DR potential of buildings. This study focused on the coupling effect of a building and an air-conditioning system. This paper describes the construction of a coupled thermal inertia model of a building and an air-conditioning system, which was validated experimentally; analyzes the accuracy of the thermal inertia of a building solely, as well as the accuracy of both the building and air-conditioning system together by comparing the model and experiment results; and quantifies the DR contributions of the thermal inertia of both the building and the air-conditioning system. A considerable demand reduction effect of the thermal inertia of the air-conditioning system was found, and the results of coupling the thermal inertia of both the building and air-conditioning system showed greater accuracy. [ABSTRACT FROM AUTHOR]

The total cost of ownership is calculated for a combined heating and humidification coil of an air-handling unit taking into account investment and operation costs simultaneously. This total cost represents the optimization function for which the minimum is sought. The parameters for the cost dependencies are the physical dimensions of the coil: length, width and height. The term 'coil' is used generically since in this setup it generates heating as well as humidification in a single unit. The first part of the paper deals with the constructive optimization and finds the relationship between the dimensions for a minimum cost. The second part of the paper takes the results of the constructive optimization further and, based on the data derived in our previous papers, analyzes the minimum total cost for the humidification coil while balancing the amount of water used to humidify the air and modify its temperature. [ABSTRACT FROM AUTHOR]

Energy consumption and air quality index (AQI) prediction is important for efficient heating, ventilation, and air conditioning (HVAC) system operation and management. A data-mining approach is presented in this paper for modeling and short-term prediction of the complicated non-linear system. The multilayer perceptron (MLP) ensemble performs best among the data mining algorithms discussed in this paper. A clustering-based method from preprocessing input data to construct the prediction models is proposed to decreases the prediction errors and the computational cost. The effectiveness of the proposed method is validated through a practical case study with both modeling and short-term prediction. The analytical results showed that the method was capable of reducing the prediction errors for modeling and short-term prediction by 11.05% and 12.21%, respectively, comparing with the models built without clustering method. [ABSTRACT FROM AUTHOR]

Primitive controllers used in the early version for HVAC systems, like the on-off (Bang-Bang) controller, are inefficient, inaccurate, unstable, and suffer from high-level mechanical wear. On the other hand, other controllers like PI and cascade controllers, overcome these disadvantages but when an offset response (inaccurate response) occurs, power consumption will increase. In order to acquire better perfor-mance in the central air-conditioning system, PIP-cascade control is investigated in this paper and compared to the traditional PI and PID, in simulation of experimental data. The output of the system is predicted through disturbances. Based on the mathe-matical model of air-conditioning space, the simulations in this paper have found that the PIP-cascade controller has the capability of self-adapting to system changes and results in faster response and better performance. [ABSTRACT FROM AUTHOR]

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

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

Heating, ventilating, and air conditioning (HVAC) units account for a significant consumption share in buildings, namely office buildings. Therefore, this paper addresses the possibility of having an intelligent and more cost-effective solution for the management of HVAC units in office buildings. The method applied in this paper divides the addressed problem into three steps: (i) the continuous acquisition of data provided by an open-source building energy management systems, (ii) the proposed learning and predictive model able to predict if users will be working in a given location, and (iii) the proposed decision model to manage the HVAC units according to the prediction of users, current environmental context, and current energy prices. The results show that the proposed predictive model was able to achieve a 93.8% accuracy and that the proposed decision tree enabled the maintenance of users' comfort. The results demonstrate that the proposed solution is able to run in real-time in a real office building, making it a possible solution for smart buildings. [ABSTRACT FROM AUTHOR]

In a central heating, ventilation, and air conditioning (HVAC) system, the set-points for several local control loops have a significant influence on the overall energy performance of the system. Real-time optimization (RtOpt) of those set-points has therefore been widely studied. However, due to the nonlinear dynamics of the HVAC system as well as the constraints associated with the system operation, real-time optimization always suffers from a heavy on-line computational load when those set-points are optimized simultaneously. To overcome this problem, multiplexed real-time optimization (MRtOpt) has been developed, which optimizes only one set-point at a time but with a faster optimization frequency. Because frequently resetting the set-points introduces artificial disturbances into the local control loops and may deteriorate the system stability, this paper presents a study to enhance the system stability of the multiplexed real-time optimization by integrating a degree of freedom (DOF) based set-point reset to renew the set-points instead of the conventional step-change set-point reset. The control performance of the integrated strategy was investigated using case studies. The results showed that around 10% of the energy saving was achieved by the proposed method compared with a method without real-time optimization. When compared with the conventional real-time optimization method, the proposed method resulted in around 70% computational load reduction, and over 26% reduction in the tracking errors of the local control loops. [ABSTRACT FROM AUTHOR]

Real-time optimization (RTO) has been developed to improve the cost and/or energy efficiency of complex heating, ventilation and air-conditioning (HVAC) systems. In current literature, almost all of the developed real-time optimization methods belong to the type of time-driven optimization, in which the action of optimization is triggered by “time”. As optimization should be done when the system operating conditions experience a change that is large enough to cause current operational setting not optimal any more, optimization strategies should recognize ‘significant’ changes and perform optimization when necessary. Since the time-driven optimization is a periodic mechanism in nature while those ‘significant’ changes may not be periodic, the time-driven optimization cannot capture ‘significant’ changes and do the optimization promptly. Therefore, this paper proposes an event-driven optimization (EDO) for complex HVAC systems, the key idea of which is to use “event” rather than “time” to trigger the action of optimization. A systematic event-driven optimization method is illustrated, where its main tasks, including event definition and event identification, will be discussed. The proposed method will be compared with a conventional time-driven method using case studies, through which the main advantages of the proposed method will be identified. [ABSTRACT FROM AUTHOR]

Real-time optimal control of an air-conditioning system is a very efficient tool to improve its energy efficient operation, which optimizes and resets on a regular time basis the set points of the local control loops that are significant for the system energy performance. Conventionally, step or rate-limited change is used to reset the set points. However, the step-change reset will always introduce significant disturbances into the control loops; while the rate-limited can reduce the disturbances but lacks systematic study as the rate is always set by a rule of thumb. As the disturbances introduced by the set point reset will not only influence the control loops that experience the reset but also be propagated to other interacted local control loops, they may severely deteriorate the stability of the whole system. This paper therefore presents a stability enhancement study for the real-time optimal control of air-conditioning systems, and proposes a degree of freedom (DOF) based set-point reset instead of the conventional step-change or rate-limited set-point reset. As the DOF based set-point reset needs disturbance models to describe the transient behaviors of the control loops activated by resetting set-points, a subspace identification method with canonical variant analysis is adopted for model identification because it has the ability to model a complex system without structural parameterization. Case studies will show that in comparison with the conventional step reset and rate-limited reset, the DOF-based set-point reset is able to enhance the stability of the whole system significantly. [ABSTRACT FROM AUTHOR]

Cooling towers are evaporative devices for removing heat in several applications such as air conditioning in buildings and industrial processes. In this work a comprehensive experiment to study the drift deposition from a mechanical draft cooling tower located in an urban environment was conducted, because of the lack of data in the literature. To predict the area affected by the cooling tower drift deposition is interesting both for its environmental impact assessment, and for the detection of the origin of an outbreak of Legionnaire's disease. The objective of the experiment was the measurement of the amount of drift water emitted and deposited from the cooling tower. Secondary objectives were to establish a database for use in drift deposition model validation and to analyze the interaction between ambient variables on downwind deposition. These objectives were met by the simultaneous measurement of cooling tower source emission parameters, meteorological variables (registered by a 40 m tall meteorological tower) and drift deposition during four test runs. The sensitive paper technique was employed. Regarding downwind deposition patterns, deposited water and characteristic droplet size decreased as the distance from the tower increased. Variations of 70% of deposited water were found in the measurements at close distances to the tower when the wind velocity level was low. Wind direction also affected the deposition level. Averaged differences of about 45% were observed between the results obtained for the wind blowing from the northwest or the southeast. [ABSTRACT FROM AUTHOR]

Free cooling is a well-known concept in the HVAC industry in which the cold water produced by a cooling tower is used directly to satisfy the requirement of the cooling load without assistance by the chiller; this concept, however, is not reported in the turbine inlet air-cooling applications. Free cooling works well as long as the ambient wet bulb temperature (WBT) is sufficiently low to produce cold water at the required temperature, but once WBT reaches its threshold value (hence, free-cooling mode is ceased) and the chiller kicks off working under its normal mode of operation, i.e., free cooling is either enabled or disabled. The proposed system in this paper provides, in addition to the above modes of operation, a novel mode that utilizes the cooling tower as a primary source of cooling simultaneously with the chiller that serves as a secondary source at elevated WBT. This new feature significantly reduces the yearly operating hours of the chiller and possibly its size, depending on the desired inlet air temperature, actual weather conditions, and design WBT. Chiller size can vary between 0% and 100% as compared to a similar classical chiller system with significant reduction in the operating hours. The proposed system consists basically of chiller, cooling tower, cooling coils, interconnecting piping, and controls. The arrangement of the system equipment changes with the operation modes in two configurations: dual water circulation loops and single water circulation loop. In the dual-loop configuration, the system has two separate loops such that the evaporator and the cooling coils are tied in one loop, while the cooling tower and condenser in the other loop; whereas in the single-loop configuration, all equipment is connected in series in one water circulation loop. This paper presents the major equipment and characteristics of the novel chiller scheme. In addition, the study outlines the potential reduction in the chiller load, size, and operating hours under a generalized weather envelope. The paper portrays the feasibility of using the proposed cooling scheme for turbine inlet air cooling. [ABSTRACT FROM AUTHOR]

Abstract: In an earlier paper [Y.H. Yau, Application of a heat pipe heat exchanger to dehumidification enhancement in tropical HVAC systems – a baseline performance characteristics study, International Journal of Thermal Sciences 46 (2) (2007) 164–171], the author had established the baseline performance characteristics of the eight-row wickless heat pipe heat exchanger (HPHX) for a vertical configuration under a range of conditions appropriate for a tropical climate. Now, the same basic experimental set-up was to be used in the present research with the HPHX tilted 30°. In this configuration, the gravitational force would be expected to enhance drainage of any condensation forming on the extended fin surfaces of the HPHX evaporator section, and therefore, the effectiveness of the HPHX could be anticipated to be better than the vertical configuration, particularly when processing inlet air with high RH. The investigation has been carried out for 32 experiments with typically high RH and the results are presented in this paper. The results suggested that the possibly adverse influence of condensate forming on the fins of the HPHX was negligible, and therefore the HPHX in a typically-used vertical configuration could perform equally as well as it would if the HPHX was installed in an inclined position. [Copyright &y& Elsevier]