Residential Heating, Ventilation, And Air Conditioning Systems Research Articles

Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurements

While automated fault detection and diagnosis (AFDD) in residential heating, ventilation, and air-conditioning (HVAC) using smart thermostat data is gaining increasing attention in recent times, it still requires in-depth investigation for market adoption, especially with real-life data. This paper proposes an Internet of Things (IoT) - based approach that adds a smart sensor to the smart thermostat data to carry out AFDD. The approach uses a model which predicts enthalpy change across the evaporator and compares the prediction to the measured enthalpy change. Deviations which exceed analytically determined thresholds then signal faults in the HVAC system. The faults detected are either installation related or degradation related. Experimental tests were carried out in four homes located in Norman, Oklahoma. From the tests, installation issues like indoor/outdoor mismatch were detected in two homes, while a 30% low charge and low indoor airflow rate were detected in one home. The results show that the proposed AFDD algorithm was able to successfully detect two prevalent faults, namely low indoor airflow and low refrigerant charge. Unlike most of the smart thermostat-based approaches, the proposed IoT-based approach can detect and diagnose both faults but only require one additional sensor which is provided by smart thermostat manufacturers.

Analysis of predicted mean vote-based model predictive control for residential HVAC systems

Model Predictive Control (MPC) is an advanced process control method that has attracted much attention in building heating, ventilation, and air conditioning (HVAC) systems. This paper analyzes the optimal precooling performance in residential buildings using MPC with two different comfort indices, namely, temperature and predicted mean vote (PMV). It first formulates, for each comfort index, an optimization problem that accounts for different factors, such as weather, home thermal condition, prediction horizon, time-of use (TOU) utility rate, and rated cooling capacity. The problem is then solved, resulting in an MPC strategy that determines the HVAC on/off control signal and minimizes energy cost over a receding time horizon while maintaining thermal comfort. The energy performance difference between temperature-based and PMV-based MPC strategies is subsequently investigated, especially in light of the interior wall surface temperature and under different combinations of the factors. Extensive simulation results demonstrated that the proposed MPC strategies are adaptive and their performances depend primarily on weather, home thermal condition, and prediction horizon, while the impact of TOU utility rate and rated cooling capacity is relatively small. Because the PMV-based MPC strategy can take advantage of the lower interior wall surface temperature due to precooling, it resulted in 8–45% cost savings for the scenarios investigated and an average increase of 0.042–0.113 in the absolute value of the PMV index compared to the temperature-based MPC strategy.

Development and evaluation of data-driven controls for residential smart thermostats

The advent of smart thermostats with real-time sensing raises the question of how to preemptively control heating, ventilation, and air conditioning (HVAC) systems to minimize energy usage while maintaining occupant comfort. To this end, we empirically compare a standard reactive deadband control to two new smart thermostat HVAC control methods: (1) a model-free reinforcement learning (RL) approach and (2) a novel model predictive control (MPC) method, whose solution is optimal with respect to its data-driven linear model. We evaluated the controls with 500 unique energy models of houses located in the United States. The models were modified to facilitate the short-term performance simulation required for residential HVAC systems. Overall, we found the MPC controller offers three distinct advantages over the RL and deadband methods: (1) MPC had the lowest average cost (defined as a custom weighted combination of runtime and comfort) of the evaluated controllers; (2) the MPC control’s linear model was able to reliably extrapolate from the sparse sample of training observations, thus enabling it to adapt quickly to recent data; and (3) in contrast to RL methods, MPC did not subject the houses or occupants to the discomfort of system exploration.

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

Residential heating, ventilation, and air conditioning (HVAC) has been considered as an important demand response resource. However, the optimization of residential HVAC control is no trivial task due to the complexity of the thermal dynamic models of buildings and uncertainty associated with both occupant-driven heat loads and weather forecasts. In this paper, we apply a novel model-free deep reinforcement learning (RL) method, known as the deep deterministic policy gradient (DDPG), to generate an optimal control strategy for a multi-zone residential HVAC system with the goal of minimizing energy consumption cost while maintaining the users’ comfort. The applied deep RL-based method learns through continuous interaction with a simulated building environment and without referring to any prior model knowledge. Simulation results show that compared with the state-of-art deep Q network (DQN), the DDPG-based HVAC control strategy can reduce the energy consumption cost by 15% and reduce the comfort violation by 79%; and when compared with a rule-based HVAC control strategy, the comfort violation can be reduced by 98%. In addition, experiments with different building models and retail price models demonstrate that the well-trained DDPG-based HVAC control strategy has high generalization and adaptability to unseen environments, which indicates its practicability for real-world implementation.

Development and Evaluation of Occupancy-Aware HVAC Control for Residential Building Energy Efficiency and Occupant Comfort

Occupancy-aware heating, ventilation, and air conditioning (HVAC) control offers the opportunity to reduce energy use without sacrificing thermal comfort. Residential HVAC systems often use manually-adjusted or constant setpoint temperatures, which heat and cool the house regardless of whether it is needed. By incorporating occupancy-awareness into HVAC control, heating and cooling can be used for only those time periods it is needed. Yet, bringing this technology to fruition is dependent on accurately predicting occupancy. Non-probabilistic prediction models offer an opportunity to use collected occupancy data to predict future occupancy profiles. Smart devices, such as a connected thermostat, which already include occupancy sensors, can be used to provide a continually growing collection of data that can then be harnessed for short-term occupancy prediction by compiling and creating a binary occupancy prediction. Real occupancy data from six homes located in Colorado is analyzed and investigated using this occupancy prediction model. Results show that non-probabilistic occupancy models in combination with occupancy sensors can be combined to provide a hybrid HVAC control with savings on average of 5.0% and without degradation of thermal comfort. Model predictive control provides further opportunities, with the ability to adjust the relative importance between thermal comfort and energy savings to achieve savings between 1% and 13.3% depending on the relative weighting between thermal comfort and energy savings. In all cases, occupancy prediction allows the opportunity for a more intelligent and optimized strategy to residential HVAC control.

In situ efficiency of filters in residential central HVAC systems.

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 situmeasurements.

Data-Driven Evaluation of Residential HVAC System Efficiency Using Energy and Environmental Data

In the U.S., the heating, ventilation, and air conditioning (HVAC) system is generally the largest electricity-consuming end-use in a residential building. However, homeowners are less likely to have their HVAC system serviced regularly, thus inefficiencies in operation are also more likely to occur. To address this challenge, this research works towards a non-intrusive data-driven assessment method using building assessors’ data, HVAC electricity demand data, and outdoor environmental data. Building assessors’ data is first used to estimate the HVAC system size, then estimate the electricity demand curve of the HVAC system. A comparison of the proposed electricity demand curve development method demonstrates strong agreement with physics-based HVAC model results. An HVAC efficiency rating is then proposed, which compares the model-predicted and actual performance data to define whether an HVAC system is operating as expected. As a case study, detailed data for 39 occupied, conditioned residential buildings in Austin, Texas, was used demonstrating the identification of the presence of potential HVAC inefficiencies. The results prove beneficial for utilities to help target residential HVAC systems in need of service or energy efficiency upgrades, as well as for homeowners as a continuous assessment tool for HVAC performance.

Measurement of residential HVAC system runtime

Recirculating central forced-air heating, ventilation, and air conditioning (HVAC) systems are common in residential buildings in North America. Runtime, the fraction of time any part of the HVAC system operates, is an important parameter to evaluate system performance and its impact on building energy use and indoor air quality. Different methods have been developed to assess runtime. In this paper, we evaluate the strengths and limitations of existing direct and indirect approaches to access runtime. Using data collected from one study in Toronto, Ontario and one in Austin, Texas, we improved the algorithm of an indirect method by Thornburg et al. (2004) which calculates conditioning runtime based on the air temperature in the supply duct. We applied the improved algorithm to two additional studies in Toronto and compared runtime results from this temperature method with other direct methods. The results show that the algorithm is reliable with a small annual underestimation (1.5–2.5% absolute difference). We also found that runtime varies considerably among similar homes with the same ambient conditions, thus it is not reliable to use outdoor temperatures to predict runtime. A further analysis on the real-time temperature and motor signals shows that the accuracy of the algorithm is influenced by system sequencing (i.e. fan and conditioning unit starting and/or stopping not being coincident in time) and fan-only operation. Overall, this work illustrates the importance to measure runtime due to its large variation and that the temperature method is a reliable approach to access runtime in appropriate homes.

Supervisory model predictive controller (MPC) for residential HVAC systems: Implementation and experimentation on archetype sustainable house in Toronto

Residential heating, ventilation and air conditioning (HVAC) systems usually employ on/off controllers to regulate the temperature. Generally, there is no supervisory controller which can use the weather forecast and electricity time of use (TOU) price information to reduce the overall operating cost of the equipment. Lack of a supervisory controller results in higher operating costs of the HVAC system. In this paper, a model predictive control (MPC) based supervisory controller is designed to shift the heating and cooling load of a house to off-peak hours. Supervisory MPC generates the temperature set-points trajectory for on/off controllers. The approach was verified through extensive experiments in an experimental archetype house. By employing the supervisory controller with variable set-points 16% cost savings were obtained when compared to the fixed zone temperature set-points at 25°C. More significant savings of about 50% were seen when MPC-based centralized controller was compared with the fixed zone temperature set-points of 24°C.

A Comparative Study of Energy Consumption for Residential HVAC Systems Using EnergyPlus

Energy conservation and sustainability have become an attractive field for research due to the growth in population and continuing search for better living standards. Heating, Ventilation, and Air Conditioning (HVAC) systems account for almost half of consumed energy in buildings and around 10 to 20% of total energy consumption in developed countries. In general, the trend of installing central HVAC systems increases in residential and commercial buildings. In this research, a study of energy consumption of HVAC systems in residential buildings has been conducted with the aim to compare those systems from an energy consumption point of view. The final goal of this research is to reduce energy requirements of residential buildings sector to save energy and reduce carbon emission. A medium size residential building in the city of Tripoli, Libya, was selected as a case study. EnergyPlus building simulation software along with OpenStudio software were used to model the house and HVAC systems. The results show that the virtual component “ideal air loads” used in EnergyPlus is very easy to use, however, its calculated energy consumption is overestimated compared to other models. Therefore, using that component can be misleading and may result in high monthly and annually energy consumption results. The results also show that in a residential building, unitary systems consume the least annual energy consumption compared to other models. It was concluded that variations in energy consumption of the considered HVAC systems decrease as the coefficient of performance (COP) increases and visa verse.

Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage

This paper describes the development, implementation and experimental investigation of a Hybrid Model Predictive Control (HMPC) strategy to control solar-assisted heating, ventilation and air-conditioning (HVAC) systems with on-site thermal energy generation and storage. A comprehensive approach to the thermal energy management of a residential building is presented to optimise the scheduling of the available thermal energy resources to meet a comfort objective. The system has a hybrid nature with both continuous variables and discrete, logic-driven operating modes. The proposed control strategy is organized in two hierarchical levels. At the high-level, an HMPC controller with a 24-h prediction horizon and a 1-h control step is used to select the operating mode of the HVAC system. At the low-level, each operating mode is optimised using a 1-h rolling prediction horizon with a 5-min control step. The proposed control strategy has been practically implemented on the Building Management and Control System (BMCS) of a Net Zero-Energy Solar Decathlon house. This house features a sophisticated HVAC system comprising of an air-based photovoltaic thermal (PVT) collector and a phase change material (PCM) thermal storage integrated with the air-handling unit (AHU) of a ducted reverse-cycle heat pump system. The simulation and experimental results demonstrated the high performance achievable using an HMPC approach to optimising complex multimode HVAC systems in residential buildings, illustrating efficient selection of the appropriate operating modes to optimally manage thermal energy of the house.

An adaptive fuzzy logic system for residential energy management in smart grid environments

Heating, Ventilation and Air Conditioning (HVAC) systems represent a significant portion of total residential energy consumption in North America. Programmable thermostats are being used broadly for automatic control of residential HVAC systems while users initialize their everyday schedules and preferences. The main aim of smart grid initiatives such as time-varying prices is to encourage consumers to reduce their consumption during high electricity demand. However, it is usually a hassle to residential customers to manually re-programme their thermostats in response to dynamic electricity prices or environmental conditions that vary over time. In addition, the lack of energy management systems such as thermostats capable of learning autonomously and adapting to users’ schedule and preference changes are major obstacles of existing thermostats in order to save energy and optimally benefit from smart grid initiatives. To address these problems, in this paper an adaptable autonomous energy management solution for residential HVAC systems is presented. Firstly, an autonomous thermostat utilizing a synergy of Supervised Fuzzy Logic Learning (SFLL), wireless sensors capabilities, and dynamic electricity pricing is developed. In the cases that the user may override the decision made by autonomous system, an Adaptive Fuzzy Logic Model (AFLM) is developed in order to detect, learn, and adapt to new user’s preferences. Moreover, to emulate a flexible residential building, a ‘house energy simulator’ equipped with HVAC system, thermostat and smart meter is developed in Matlab-GUI. The results show that the developed autonomous thermostat can adjust the set point temperatures of the day without any interaction from its user while saving energy and cost without jeopardizing user’s thermal comfort. In addition, the results demonstrate that if any change(s) occurs to user’s schedules and preferences, the developed AFLM learns and adapts to new modifications while not ignoring energy conservation aspects.

Effects of dead-band and set-point settings of on/off controllers on the energy consumption and equipment switching frequency of a residential HVAC system

Residential heating, ventilation and air conditioning (HVAC) systems generally employ simple on/off controllers to regulate the temperature of water and air in different subsystems. Selection of set-point of a controlled process and dead-band of controller affects the process regulation, energy consumption and actuator switching frequency. This article presents a calibrated model of a residential HVAC system. Temperature of two zones and buffer tank (BT) is regulated using on/off controllers. Non optimum controller settings result in poor regulation, higher energy consumption and higher equipment wear. The purpose of this article is to find optimum set-point and dead-band settings for on/off controllers in order to improve temperature regulation, reduce energy consumption and decrease equipment wear by reducing the switching frequency of HVAC equipment without scarifying thermal comfort of occupants.

Data-Driven Methodology for Energy and Peak Load Reduction of Residential HVAC Systems

Residential buildings in the United States are responsible for the consumption of approximately 38% of electricity, and for much of the fluctuations in the power demands on the electric grid, particularly in hot climates. Residential Heating, Ventilation, and Air Conditioning (HVAC) systems are one of the largest electricity users of homes in these regions. “Smart” technologies, including electric grid-connected devices and home energy monitoring systems are increasingly available and installed in buildings, enabling new, data-driven methodologies for the operation of smarter, more sustainable building systems. This research investigates the use of residential energy use data and smart connected thermostat data to continuously monitor the health and performance of residential HVAC systems. Using field-collected HVAC energy consumption and performance data to develop a process-history based model, the results of this research suggest that the use of this methodology can save up to 6% of annual energy use of residential buildings.

Black-box modeling of residential HVAC system and comparison of gray-box and black-box modeling methods

In this article, black-box models of the residential heating, ventilation and air conditioning (HVAC) system are developed. The data of the input and output of the system is measured and the models of the energy recovery ventilator (ERV), air handling unit (AHU), buffer tank (BT), radiant floor heating (RFH) and ground source heat pump (GSHP) are developed using the system identification techniques in Matlab®. The developed models include models based on multiple-input and multiple-output (MIMO) artificial neural network (ANN), transfer function (TF), process, state-space (SS) and autoregressive exogenous (ARX) ones of each HVAC subsystem (ERV, AHU, BT and RFH). The gray-box models of the same HVAC subsystems were developed in [1] which are also compared with the black-box models developed in this article. The models were compared visually and analytically. Ranks of the models were calculated based on their relative performance. It was found that the ANN outperforms the other modeling methods followed by the ARX, TF, SS, process and gray-box models respectively.

Gray-box modeling and validation of residential HVAC system for control system design

In this paper gray-box models of the residential heating, ventilation and air conditioning (HVAC) system were developed. The HVAC system comprises of several subsystems such as energy recovery ventilator (ERV), air handling unit (AHU), buffer tank (BT), radiant floor heating (RFH) system, zone and ground source heat pump (GSHP) whose models can be identified separately and combined to obtain the model of the full system. The parameters of the subsystem models were identified from the data measured from the instrumented TRCA Archetype Sustainable House (TRCA-ASH) HVAC systems located at Kortright Centre for Conservation in Vaughan, Ontario, Canada. Individual subsystem models were combined to obtain the full system model which replicates the performance of the existing HVAC system and provides the cost estimate for running the HVAC system. Existing HVAC system uses ON/OFF controllers for zone temperature and BT temperature control. The ON/OFF controllers were integrated into the full scale system model and energy estimates were calculated for the operation of primary and secondary components (e.g., GSHP, fans and pumps). This model can be used to further investigate the effects of more advanced controllers (e.g., PID, model predictive control-MPC) and energy conservation strategies (e.g., set-point reset, passive/active thermal energy storage) in the simulation before implementing on the existing HVAC system.