Residential heat pumps have advanced over the past decade to allow for operation at colder temperatures. However, the challenges of frost accumulation and defrosting the outdoor coil remain. The goal of this study was to evaluate the impact of the control algorithms that determine when a heat pump needs to defrost and when the base pan heater runs on the overall heating efficiency of the heat pump. In this study, which occurred during the 2023–2024 heating season, we measured the performance of a ductless air-source heat pump installed in Fairbanks, Alaska, USA. The heat pump was instrumented to measure the electrical input and the thermal output, as well as selected internal variables and indoor and outdoor environmental conditions. The heat pump was first operated with factory default control algorithms associated with the initiation of defrost and control of the base pan heater. These factory default algorithms focused on aggressively defrosting the outdoor coil and keeping the base pan ice-free. In the middle of the winter, these algorithms were changed to focus on reducing defrost cycles and increasing efficiency, while the heat pump continued to be operated and monitored. The results showed that significant increases in efficiency are possible by improving the defrost and base pan heater control algorithms.

This paper investigates the auxiliary heat use for air-source heat pumps (ASHPs) operating in cold climates. Twelve variable-capacity, central ducted ASHPs installed in single-family homes in cold climate regions (eleven in the northwest United States and one in a Denver suburb) were monitored for an entire winter season to collect data at cold temperatures. The methodology employed airside and power measurements that were taken every five seconds, to calculate the heat pump's capacity, coefficient of performance (COP), and auxiliary heat energy consumption. This paper provides insights into the practical implications of auxiliary heat utilization in centrally ducted ASHPs and suggests opportunities to mitigate the usage of auxiliary heat, improving overall system efficiency during cold climate operation.

This study investigates the impact of refrigerant undercharge on indoor temperature and HVAC system performance in residential buildings. Simulation models for typical residential buildings in Orlando, FL and Indianapolis, IN were developed using the ResStock database. A refrigerant undercharge fault model was then applied to the simulations with varying levels of fault intensity. The paper offers an extensive analysis, revealing that variations in supply air temperature, equipment runtime, and cooling energy consumption due to the level of refrigerant undercharge faults are notably significant on a summer representative day. Similarly, on a winter representative day, changes in supply air temperature and runtime are significant as well as changes in supplemental heat energy consumption. We find that occupants may remain oblivious to these faults during the cooling season, particularly when the HVAC system is oversized; in that case, supply air temperature data could help detect a fault. Another challenge is that during the heating season, when the supplemental heater operates, it is difficult to identify a refrigerant undercharge fault using only indoor and supply air temperature data. This study finds that supply air temperature, equipment runtime, and supplemental heater energy consumption data can help in detecting refrigerant undercharge faults.

The state of the art of fault detection and diagnosis (FDD) for residential air-conditioning systems is expensive and not yet amenable to widespread implementation. FDD for homes can significantly reduce utility costs, and increase the lifespan of the equipment. The cost barriers currently, however, make FDD for homes economically unviable for large scale implementation. In prior work, we offered a solution to reduce FDD costs by proposing an automated fault detection algorithm to serve as a screening step before more expensive FDD tests can be conducted. The algorithm uses only the home thermostat and local weather information to identify thermodynamic parameters and detect high-impact air-conditioning faults, including those that occur during equipment installation. We had tested the algorithm on a single EnergyPlusTM model of a home in Orlando, Florida. The thermodynamic parameter identification process is highly nonconvex involving several local optimal solutions. In this paper we propose a novel method to select the best model for fault detection from among the list of local optimal solutions to make the algorithm more robust to homes of different construction, without which the fault detection process would be infeasible. Another unique contribution of the paper is implementing the solution on real-world data. We also bring the algorithm closer to market by testing it on real-world data. We implement the algorithm on data obtained from experiments conducted by the Florida Solar Energy Center (FSEC) on a laboratory home equipped with a heat pump where faults were intentionally added for a period of seven months. The algorithm successfully detected an undercharge fault with 70.6% accuracy, concurrent duct leakage and undercharge faults with 85.2% accuracy, and duct leakage faults with 69.1% accuracy. A sensitivity analysis is also performed on EnergyPlus models of nine types of homes that vary in construction to demonstrate the robustness of the algorithm. The algorithm achieves an average accuracy of 71% for no-fault condition, 77% for 40% undercharge fault, and 76% for duct-leak fault.

Heating energy is the largest end-use for U.S. residential buildings accounting for approximately one-third of residential building energy consumption (EIA 2021). Historically, air-source heat pumps have been limited to temperate climates because of subpar performance at extremely cold outdoor air temperatures. However, recent advances to cold-climate air-source heat pump technology, which typically rely on inverter-driven, variable-speed compressors and variable-speed fans, have significantly improved low-temperature heat pump performance enabling the technology to save energy for many homes in cold climates. The primary objective of this project was to measure in-field performance of centrally ducted, variable-capacity air-source heat pumps in cold climates to validate performance and develop field-based performance maps. The project focused on quantifying heat pump performance at cold temperatures. The sites identified for the study were primarily located in the Northwest United States since homes in the region tend to have all-electric space heating systems and high-efficiency heat pumps have been incentivized in the region for several years. NREL partnered with Ecotope, Inc., a small energy consulting firm located in Seattle, WA, for site recruitment, monitoring equipment installation, data quality management. All the sites included in the study had previously installed a high-efficiency, central heat pump system. One site was in a Denver, CO suburb, which was the only dual fuel heat pump in the study. We used airside and power measurements, collected at 5-second intervals, to quantify heat pump capacity, coefficient of performance (COP), and auxiliary heater energy consumption. We developed algorithms to automatically determine the heat pump operating mode including defrost and auxiliary heating operation. A whole-house thermal and duct audit was completed during the initial site visit to estimate winter heating loads and assess heat pump sizing. Whole-home heating design loads were calculated at ASHRAE 99% design temperatures and compared to manufacturer-reported maximum capacities to assess the heat pump sizing at each site.

Faults in residential heating, ventilating, and air conditioning (HVAC) equipment may occur due to poor installation practices or develop over time, and these faults can negatively impact system efficiency, thermal comfort, and equipment lifespan. Automated fault detection and diagnostic (AFDD) technologies identify energy wasting HVAC faults, such as low indoor airflow and improper refrigerant charge, and guide technicians in improving system efficiency. For residential HVAC, AFDD consists of a range of fault detecting and diagnostic capabilities, sensor configurations, and target applications. AFDD technology can either be permanently installed by the original equipment manufacturer (OEM) using embedded sensors or as an add-on product either during or after installation. Additionally, several advanced installation tools and refrigerant gauge sets include AFDD features for temporary use during equipment installation and tune-ups. Some technologies can detect a fault but have limited diagnostic capabilities. For example, a single-point measurement from the home's thermostat or energy monitor can provide certain fault detection capability by analyzing the equipment runtime or energy consumption. These technologies, though limited at determining the cause of a given fault, may have significant energy savings potential due to their low cost and prevalence in the residential HVAC market. Despite the potential benefits, fault detection technologies face many technical and market barriers preventing broad adoption. Beyond the cost barriers due to the added sensor requirements and technology development, fault detection technologies face many implementation and adoption barriers such as installer training, customer awareness, standardized communication protocols, and methods of test for evaluating accuracy. The purpose of this whitepaper is to characterize market and technical barriers impeding broader utilization of fault detection technology for residential HVAC energy efficiency applications.

This project was funded by SCE's Emerging Technologies Program, and evaluates the feasibility of a connection system that can pave the path to accelerated, cost-effective adoption of high-efficiency Minisplit Heat Pump (MSHP) systems. This report details the characterization of this connection system and a heat pump enabled with this technology in a laboratory setting, and assesses the energy and cost impact applicable to residential buildings in SCE service territory. The energy and economic analysis leverages NREL's ResStock (TM) modeling approach based on the EnergyPlus (R) hourly simulation platform. The connection system can be incorporated into existing MSHP architectures. This reduces installation time from 10 to 20 man hours to approximately one hour or less, dramatically lowering total installation costs without adversely impacting heat pump performance. This report characterizes the connection system's leakage performance while connected and disconnected, as well as leakage over several connection/disconnection cycles, commensurate with manufacturer specifications for the individual components. The connector was incorporated into an off-the-shelf MSHP. Performance was compared to an identical unmodified heat pump. The connector had no impact on performance. Large-scale hourly energy simulations were performed for 22,574 homes across 15 counties in SCE service territory. The analysis was performed using baseline assumptions about the penetrations of window air conditioners (~18%) and six upgrade scenarios for these air conditioners. All scenarios assumed 100% adoption of the Connector-Supported Heat Pump (CSHP) in place of window air conditioners: MSHPs at Seasonal Energy Efficiency Ratio (SEER) 17, 25, and 33, and CSHPs at SEER 17, 25, and 33. Results show the increased adoption of high-efficiency heat pumps can result in up to 29% air conditioning energy savings for homes with window air conditioners.

Residential air conditioning equipment comprises a significant portion of the total energy consumption of a home. Unfortunately, air-conditioning systems can be susceptible to faulty operation either from installation errors or faults that accrue over the equipment's lifetime. This paper presents a novel automated fault detection algorithm for residential air-conditioning systems that can alert the homeowner of the presence of these faults. The proposed algorithm utilizes only the home's thermostat and outside air temperature to perform automated fault detection over the course of the equipment's lifetime, including immediately after installation. The algorithm uses an extended Kalman filter approach to identify a three-resistor, two-capacitor (3R2C) electrical equivalent thermodynamic model. The identified 3R2C model is used to predict cooling times during a testing period comprising of a series of thermostat drive-cycle experiments. We tested the algorithm on an EnergyPlus (TM) model of a typical residential building in Orlando, Florida. Duct faults, indoor airflow faults, and refrigerant undercharge faults were introduced into the building model one at a time. The algorithm was able to accurately determine duct-leak faults, 40% airflow faults, 40% undercharge faults, and no-fault cases with an accuracy of 70%, 77%, 82%, and 87%, respectively. (C) 2021 Elsevier B.V. All rights reserved.

To ensure that new residential air conditioners and heat pumps operate optimally, care must be taken during the initial system design and installation to avoid missteps that immediately degrade a system's performance. Laboratory and simulation studies have quantified the impact that faults can have on capacity and efficiency. Field studies have demonstrated that installation-related faults are commonplace. However, the national impact of installation-related faults cannot be accurately estimated by only simulating a limited number of homes at several fault levels because of the variety of home characteristics and climates involved. In our analysis, we use an improved residential building stock simulation tool to predict the annual energy increase and additional utility costs resulting from two common installation faults: indoor airflow rate and refrigerant charge level. Our method considers the wide range of building characteristics and climate zones of the U.S. housing stock. We use existing field data to develop fault intensity probability distributions to inform our analysis. The analysis shows that these two faults result in approximately 20.7 TWh/y of additional energy use for central air conditioners and air-source heat pumps in U.S. single-family detached homes, which is a 9% increase over baseline (no-fault) usage, costing homeowners approximately $2.5 billion annually on utility bills. Air-source heat pumps are responsible for a disproportionate fraction of this energy use increase because of the larger number of operating hours compared to central air conditioners and the sensitivity of heating mode performance to the faults analyzed compared to cooling mode.

Residential building energy consumption in the United States has decreased steadily during the last several decades largely because of advances in building codes as well as voluntary efficiency and labeling programs, which have resulted in the reduction in building sensible loads. Building latent loads have not decreased by the same amount, and field and analytical research studies have concluded that high-efficiency, low-load homes often have elevated indoor humidity levels, possibly leading to occupant discomfort. There are discrepancies in the literature on occupant comfort as it relates to indoor humidity and recommended upper humidity limits. The choice of humidity limit could potentially change results and conclusions on the energy impacts of a new air conditioner or dehumidifier technology, changing ventilation rates, or an improved building envelope. In this study, we use building simulations to explore the impact of humidity control on cooling energy use in efficient residential buildings for different humidity limits. We look at limits based on humidity ratio (or dew point temperature), wet-bulb temperature, relative humidity, and constant slope lines based on the popular comfort models of both Fanger and Gagge. We quantify the additional energy use required to control humidity below these limits. We look at the sensitivity of the results to different temperature and humidity set points, occupant internal gains, moisture buffering levels, evaporator airflow rates, and dehumidifier energy factors. High-efficiency home predicted energy savings are more sensitive to the assumed humidity limit and cooling and dehumidification set points than the other parameters investigated in this paper.

Moisture buffering of building materials has a significant impact on the building's indoor humidity, and building energy simulations need to model this buffering to accurately predict the humidity. Researchers requiring a simple moisture-buffering approach typically rely on the effective-capacitance model, which has been shown to be a poor predictor of actual indoor humidity. This paper describes an alternative two-layer effective moisture penetration depth (EMPD) model and its inputs. While this model has been used previously, there is a need to understand the sensitivity of this model to uncertain inputs. In this paper, we use the moisture-adsorbent materials exposed to the interior air: drywall, wood, and carpet. We use a global sensitivity analysis to determine which inputs are most influential and how the model's prediction capability degrades due to uncertainty in these inputs. We then compare the model's humidity prediction with measured data from five houses, which shows that this model, and a set of simple inputs, can give reasonable prediction of the indoor humidity. (C) 2018 Elsevier B.V. All rights reserved.

Increasing insulation levels and improved windows are reducing sensible cooling loads in high-efficiency homes. This trend raises concerns that the resulting shift in the balance of sensible and latent cooling loads may result in higher indoor humidity, occupant discomfort, and stunted adoption of high-efficiency homes. This study utilizes established moisture-buffering and air-conditioner latent degradation models in conjunction with an approach to stochastically model internal gains. Building loads and indoor humidity levels are compared for simulations of typical new construction homes and high-efficiency homes in 10 US cities. The sensitivity of indoor humidity to changes in cooling set point, air-conditioner capacity, and blower control parameters are evaluated. The results show that high-efficiency homes in humid climates have cooling loads with a higher fraction of latent loads than the typical new construction home, resulting in higher indoor humidity. Reducing the cooling set point is the easiest method to reduce indoor humidity, but it is not energy efficient, and overcooling may lead to occupant discomfort. Eliminating the blower operation at the end of cooling cycles and reducing the cooling airflow rate also reduce indoor humidity and with a smaller impact on energy use and comfort.

Residential building codes and voluntary labeling programs are continually increasing the energy efficiency requirements of residential buildings. Improving a building's thermal enclosure, installing the ductwork in conditioned space, and improving the building's airtightness results in significant reductions in externally-driven sensible and latent cooling loads. As a building's efficiency is improved, occupant-related internal gains become a larger portion of the building sensible and latent loads. Additionally, internal gains are highly uncertain compared to other load components. In this study, we use a stochastic approach to simulate occupant-related internal gains and compare the internal gains to other sensible and latent heat sources in four house efficiency levels in 10 U.S. climates using whole-building energy simulation software. We compare the expected range in occupant-related internal gains to other building characteristics such as cooling set point, air infiltration rate, and mechanical ventilation rate. We show that in high-efficiency homes, sensible internal gains vary from less than 10% to greater than 40% of the building sensible load under peak total cooling conditions depending on climate and internal gain profile. Likewise, latent internal gains vary from less than 10% to more than 60% of the building latent load under peak total cooling and peak dehumidification conditions depending on climate and internal gain profile. (C) 2018 Elsevier B.V. All rights reserved.

The key hurdles to achieving wide consumer acceptance of battery electric vehicles (BEVs) are weather-dependent drive range, higher cost, and limited battery life.These translate into a strong need to reduce a significant energy drain and resulting drive range loss due to auxiliary electrical loads the predominant of which is the cabin thermal management load.Studies have shown that thermal subsystem loads can reduce the drive range by as much as 45% under ambient temperatures below -10 °C.Often, cabin heating relies purely on positive temperature coefficient (PTC) resistive heating, contributing to a significant range loss.Reducing this range loss may improve consumer acceptance of BEVs.The authors present a unified thermal management system (UTEMPRA) that satisfies diverse thermal and design needs of the auxiliary loads in BEVs.Demonstrated on a 2015 Fiat 500e BEV, this system integrates a semi-hermetic refrigeration loop with a coolant network and serves three functions: (1) heating and/or cooling vehicle traction components (battery, power electronics, and motor) (2) heating and cooling of the cabin, and (3) waste energy harvesting and re-use.The modes of operation allow a heat pump and air conditioning system to function without reversing the refrigeration cycle to improve thermal efficiency.The refrigeration loop consists of an electric compressor, a thermal expansion valve, a coolant-cooled condenser, and a chiller, the latter two exchanging heat with hot and cold coolant streams that may be directed to various components of the thermal system.The coolant-based heat distribution is adaptable and saves significant amounts of refrigerant per vehicle.Also, a coolant-based system reduces refrigerant emissions by requiring fewer refrigerant pipe joints.The authors present bench-level test data and simulation analysis and describe a preliminary control scheme for this system.

Moisture adsorption and desorption in building materials impact indoor humidity. This effect should be included in building-energy simulations, particularly when humidity is being investigated or controlled. Several models can calculate this moisture-buffering effect, but accurate ones require model inputs that are not always known to the user of the building-energy simulation. This research developed an empirical method to extract whole-house model inputs for the effective moisture penetration depth (EMPD) model. The experimental approach was to subject the materials in the house to a square-wave relative-humidity profile, measure all of the moisture-transfer terms (e.g., infiltration, air-conditioner condensate), and calculate the only unmeasured term the moisture sorption into the materials. We validated this method with laboratory measurements, which we used to measure the EMPD model inputs of two houses. After deriving these inputs, we measured the humidity of the same houses during tests with realistic latent and sensible loads and demonstrated the accuracy of this approach. These results show that the EMPD model, when given reasonable inputs, is an accurate moisture-buffering model. (C) 2016 Elsevier B.V. All rights reserved.

This multiphase study involved comprehensive comparative testing of EnergyPlus and SEEM to determine the differences in energy consumption predictions between these two programs and to reconcile prioritized discrepancies through bug fixes, modeling improvements, and/or consistent inputs and assumptions.

Residential building codes and voluntary labeling programs are continually increasing the energy efficiency requirements of residential buildings. Improving a building's thermal enclosure and installing energy-efficient appliances and lighting can result in significant reductions in sensible cooling loads leading to smaller air conditioners and shorter cooling seasons. However due to fresh air ventilation requirements and internal gains, latent cooling loads are not reduced by the same proportion. Thus, it's becoming more challenging for conventional cooling equipment to control indoor humidity at part-load cooling conditions and using conventional cooling equipment in a non-conventional building poses the potential risk of high indoor humidity. The objective of this project was to investigate the impact the chosen design condition has on the calculated part-load cooling moisture load, and compare calculated moisture loads and the required dehumidification capacity to whole-building simulations. Procedures for sizing whole-house supplemental dehumidification equipment have yet to be formalized; however minor modifications to current Air-Conditioner Contractors of America (ACCA) Manual J load calculation procedures are appropriate for calculating residential part-load cooling moisture loads. Though ASHRAE 1% DP design conditions are commonly used to determine the dehumidification requirements for commercial buildings, an appropriate DP design condition for residential buildings has not been investigated. Two methods for sizing supplemental dehumidification equipment were developed and tested. The first method closely followed Manual J cooling load calculations; whereas the second method made more conservative assumptions impacting both sensible and latent loads.