Engineering topics

Munk, Jeffrey

Publications and source records attributed to Munk, Jeffrey.

17 records

DOE OSTI2024

Residential Cold Climate Heat Pump Technology Challenge (Rheem) (CRADA NFE-22-09374 Final Report)

The purpose of this CRADA was to test a nominal 3-ton capacity heat pump system supplied by Rheem Manufacturing to determine if it met the laboratory test requirements of the United States Department of Energy Cold Climate Heat Pump (CCHP) Technology Challenge (hereafter noted as “the Challenge”). Results of the laboratory testing completed at ORNL demonstrated that the system met all requirements of the Challenge. Rheem Manufacturing has successfully completed this aspect of the Challenge. This CRADA report will briefly describe the CCHP Challenge, the test plan created to measure the success of heat pump systems provided by CCHP Challenge participants, and the results of laboratory testing of the Rheem heat pump system.

42 ENGINEERING↗

DOE OSTI2024

Impact of refrigerant undercharge faults on building indoor conditions and HVAC system operation in residential Buildings: A simulation study

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. Finally, this study finds that supply air temperature, equipment runtime, and supplemental heater energy consumption data can help in detecting refrigerant undercharge faults.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Autonomous Anomaly Detection for MPC Forecasts of HVAC Systems in Residential Communities

The use of residential heating, ventilation, and air conditioning (HVAC) to shift peak demand or provide ancillary services is a potential solution in the presence of older grids and distributed renewables. However, to ensure the efficient use of devices, utilities need to accurately forecast the load and adopt error correction schemes when necessary. While significant theoretical research exists in the area of predictive control of HVAC, little experimental evidence exists. The lack of experimental data in turn causes researchers to be unprepared for unsystematic errors which emerge due to the higher complexity of the data generating process. This study offers an anomaly detection methodology that uses unsupervised machine learning algorithms to detect and isolate these errors with different forecast error ranges. The results of anomaly detection procedure can then be used for error correction and would eventually help develop better predictive controllers. The methodology is tested using real world data from a smart neighborhood that currently operates in Atlanta. GA.

Lebakula, Viswadeep↗

DOE OSTI2024

Structural Differences between Morning and Evening Peak in Optimized Water Heaters

Peak reduction is an important concern that can help reduce the growing stress on distribution grids and allow to defer investments in new capacity. Water heaters represent a convenient way of reducing peak, depending on controllability of devices. But while controlling water heaters does allow to shift peak, it also results in rebound effects, which require additional understanding before water heater fleets can be used on a large scale. We attempt to investigate the nature of peak behaviors of water heaters and demonstrate that water heaters are not homogenous in their behavior. Depending on the overall intensity of the use of water, part of the population has higher rebound effect, while part of the population has little or no rebound effect. Even though we do not have sufficient data to statistically evaluate our findings, we use a sample of 42 water heaters in a connected neighborhood to provide an early attempt at discovering and reporting this diversity.

Tsybina, Eve↗

DOE OSTI2023

Anomaly detection for MPC forecast in Fleet of Water Heaters

Among residential devices, water heaters consume 20% of home energy use in the United States. Water heaters possess the capability to store energy within their reservoirs, enabling the ability to decouple energy use from hot water use. This capability can be used to reduce energy usage and costs while also supporting grid services. This requires accurate forecasting of the parameters of the water heater such as upper and lower temperatures. In this study, we analyzed the performance and behavior of a water heater model used in the real-world to predict a control mechanism that is implemented in a smart residential neighborhood. The model forecasts are accurate in most cases but not all. In such scenarios, error correction of the model is necessary to further improve model predictive control accuracy. Anomaly detection is the first step of error correction. This study complements existing research by grouping time series data into two clusters one with anomalies and another without anomalies. To achieve this task, we explored and compared multiple unsupervised machine learning algorithms to perform clustering. Among these algorithms, Ward clustering has the lowest running time and identified the highest number of anomalies for the upper temperature limit. The proposed approach is tested based on the data collected in a neighborhood with 46 townhomes located in Atlanta, GA.

Lebakula, Viswadeep↗

DOE OSTI2023

Impact of Control on Availability and Cycling of Residential HVAC in a Real-World Experiment

Demand response is an important emerging part of smart grids and there is a large stream of research from theoretical and modeling perspectives. However, there is relatively little experimental evidence that could help researchers and building operators make informed decisions on best practices for modeling, developing, and deployment of control mechanisms. We contribute to the body of experimental research by providing numerical insights into the role and availability of residential HVAC systems for control. We share the findings on the duration of the cooling cycle, off cycle, and temperature settling time of HVAC systems from data collected from a smart neighborhood located in Atlanta, GA.

cycling↗

DOE OSTI2023

The Empirical Effect of Fleet Optimization on Synchronization and Rebound Effects in Heat Pump Water Heaters

Demand response is a growing concept in light of the internet of things and an increasing need for grid flexibility. Water heaters are one of the preferred devices for providing demand response for grid services and peak management due to their capability to store energy. The efficient use of water heaters for demand response requires consideration of the associated load effects such as synchronization of device schedules and rebound effect. These effects present a significant challenge. Despite the importance of the mentioned effects for water heater queuing and scheduling, there has been no effort to quantify and empirically validate their impact. This study attempts to address this gap by offering two methods - Ward clustering and Euclidean K-means - to evaluate the extent of synchronization in a fleet of 42 water heaters in Atlanta, GA. Using the aforementioned methods on the measured data, we find evidence of convergence of water heater loads as a result of optimization compared to an idle period and analyzed their impact.

demand response↗

DOE OSTI2023

Peak Reduction Using Mode Adjustment of Heat Pump Water Heaters in a Residential Neighborhood

Building electrification is putting pressure on distribution grid worldwide. Peak reduction is an important concern that can help reduce the growing stress and allow to defer investments in new capacity. Water heaters represent a convenient way of reducing peak because they are less dependent on weather, and their storage volume allows for asynchronous water heating and hot water use. Previous empirical studies investigated the ability of water heaters to reduce peak through the adjustment of the temperature setpoint. However, not all equipment vendors offer this option. This study aims at understanding the feasibility of peak reduction with an alternative configuration available in the market - by adjusting the device mode rather than temperature setpoint. The peak reduction methodology is tested in an occupied 46-townhome neighborhood located in Atlanta, GA. We find that peak shifting is possible with the adjustable mode approach, with the change in the peak load by 30-60%.

demand response↗

DOE OSTI2023

Experimental Evidence on Latency in a Fleet of Controllable Water Heaters

Demand response is an important emerging part of smart grids with wide coverage in theoretical and modeling research. However, experimental evidence on the real-life behavior of controllable loads is still limited. We present observations regarding latency and communication aspects of the operation on a fleet of residential water heaters in a smart neighborhood in Atlanta, GA. Our analysis shows that latency in water heaters is not constant and does not follow a Gaussian distribution. We also find that there is a systematic relationship between latency and hour of the day. Latency was found to increase during morning and evening hours compared to the afternoon. These findings could help better plan deployment of control for demand response programs. Understanding delays associated with controlling smart devices is crucial for proper design and algorithm development for optimization, frequency of dispatch, and override detection.

communication delay↗

DOE OSTI2023

Using Synchronization as an Indicator of Controllability in a Fleet of Water Heaters

Peak reduction is an important concern that can help reduce the growing stress on distribution grid and allow to defer investments in new capacity. However, the growing concern for customer privacy and comfort may impact the performance of load control for residential devices. Water heaters represent a convenient way of reducing peak due to their ability to store thermal energy for future use. In this paper, we developed a methodology to help utilities gain more insight with respect to the impact of load control efforts for shaving peak with no necessary information about the water heaters except the device status (on/off). To this end, we use a fleet of water heaters in a controlled residential neighborhood in Atlanta, GA. Our findings show that convergence in device status can serve as a proxy for peak shifting during hours of the evening peak.

demand response↗

DOE OSTI2023

Deep Reinforcement Learning Based Smart Water Heater Control for Reducing Electricity Consumption and Carbon Emission

Water heating is the third largest electricity consumer in U.S. households, after space heating and cooling. Thus, water heaters represent a significant potential for reducing electricity consumption and associated CO2 emissions of residential buildings. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity consumption and the CO2 emissions of a heat pump water heater without affecting user comfort. In this approach, a set of RL agents focusing on either electricity saving or emission reduction, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on different hot water usage and Marginal Operating Emissions Rate (MOER) profiles. The testing results showed that the RL agents that focus on electricity saving can save electricity in the range of 12–22% by operating the water heater with maximum heat pump efficiency and minimum electric element utilization. On the other hand, the RL agents that focus on emission reduction reduced emissions in the range of 18–37% by making use of the variable MOER values. These RL agents used the heat pump and/or an element when the MOER values are low due to the availability of renewable energy sources (e.g., solar and wind) and mostly avoided the periods of carbon-intensive periods. Overall, these results showed that the proposed RL approach can help minimize the electricity consumption and the CO2 emissions of a heat pump water heater without having any prior knowledge about the device.

Amasyali, Kadir↗

DOE OSTI2023

Field Performance of R-1234yf Heat Pump Water Heaters

Heat pump water heaters (HPWH) provide a resource for increasing water heating efficiency in U.S. residences. Electric HPWHs have traditionally utilized R-134a as the refrigerant in the vapor-compression cycle; however, this refrigerant is undesirable long-term due to its high global warming potential. For HPWHs, low global warming potential refrigerants such as R-1234yf may offer comparable performance, but the evaluation of these systems has been limited to laboratory settings. This study provides field evaluations of off-the-shelf HPWHs that had their factory R-134a refrigerant replaced with an optimized charge of R-1234yf. The field evaluation consisted of R-1234yf HPWHs at two occupied field sites and two unoccupied, simulated lab homes. At the two residential field sites with occupants, the R-1234yf HPWHs operated issue-free for the 18-month field trial, and the home occupants perceived no change in HPWH performance relative to their prior R-134a HPWHs. At the lab homes, under simulated hot water draws, the average daily operating efficiency of the R-1234yf HPWH was within 2% of the baseline R-134a HPWHs.

42 ENGINEERING↗

DOE OSTI2023

Deep Reinforcement Learning Based HVAC Control for Reducing Carbon Footprint of Buildings

In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Operating Emission Rates (MOER), where the objective was to shift the demand to the low emission period of the day and 2) Time-Of-Use (TOU) demand-response price where the objective was to shift the demand to low price period of the day. This was achieved by learning an optimal pre-cooing strategy. We found the carbon emission reduction in the range of 6%-16% depending on the opportunity presented by the MOER signal. Similarly, we observed the carbon emission reduction in the range of 23%-29% during the peak price period when TOU price was used. The results clearly demonstrated the applicability of our approach in reducing the carbon footprint of the building.

carbon emission↗

DOE OSTI2023

Reinforcement-Learning-Based Smart Water Heater Control: An Actual Deployment

Utilizing smart control algorithms for electric water heaters (EWHs) is essential for fully harnessing the demand response (DR) potential of EWHs. For this reason, the use of reinforcement learning (RL) algorithms for EWHs has received increasing attention in recent years. However, existing RL approaches are either simulation-based or use pretrained RL agents. To this end, this paper presents the real-world deployment of a set of model-free RL approaches that aim to minimize the electricity cost of a EWH under a time-of-use electricity pricing policy using standard DR commands (e.g., shed, load up). The experiment results showed that the RL agents can help save electricity cost in the range of 11% to 14% compared to the baseline operation. This study demonstrated that RL-based EWH controllers can be deployed in real world without any prior training and can still save electricity cost.

deep learning↗

DOE OSTI2022

Cold Climate Heat Pump Evaluation

The use of heat pumps in cold and very cold climates increased by 63% between 2009 and 2015 [1, 2]. This can be attributed to development of heat pumps with higher heating capacity at lower temperatures and increased efficiency. A primary technology driving this trend is the use of variable-speed compressors, which come with an increase in the level of controls in the equipment. Most units with variable-speed compressors also include electronic expansion valves and variable-speed indoor (ID) and outdoor (OD) fans. With the increased complexity of these systems, developing a test procedure that is representative of typical performance and not too burdensome on manufacturers presents a difficult challenge.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Deep reinforcement learning with online data augmentation to improve sample efficiency for intelligent HVAC control

Deep Reinforcement Learning (DRL) has started showing success in real-world applications such as building energy optimization. Much of the research in this space utilized simulated environments to train RL-agent in an offline mode. Very few research have used DRL-based control in real-world systems due to two main reasons: 1) sample efficiency challenge---DRL approaches need to perform a lot of interactions with the environment to collect sufficient experiences to learn from, which is difficult in real systems, and 2) comfort or safety related constraints---user's comfort must never or at least rarely be violated. In this work, we propose a novel deep Reinforcement Learning framework with online Data Augmentation (RLDA) to address the sample efficiency challenge of real-world RL. We used a time series Generative Adversarial Network (TimeGAN) architecture as a data generator. We further evaluated the proposed RLDA framework using a case study of an intelligent HVAC control. With a ≈28% improvement in the sample efficiency, RLDA framework lays the way towards increased adoption of DRL-based intelligent control in real-world building energy management systems.

Kurte, Kuldeep↗

DOE OSTI2022

Seamlessly Fuel Flexible Heat Pump with Optimal Model-based Control Strategies to Reduce Peak Demand, Utility Cost and CO2 Emission

This research develops a novel hybrid fuel heat pump system for space heating of residential and small commercial buildings with built-in optimization and control. Whereas conventional dual fuel systems either run on gas or electricity at any given moment, the proposed seamlessly fuel flexible heat pump (SFFHP) simultaneously consumes gas and electricity and continuously optimizes the proportion of each. The building air flows across the heat pump condenser first and then flows across the furnace coil, and this reduces the heat pump temperature lift. The SFFHP delivers energy savings by allowing each subsystem (gas furnace and electric heat pump) to operate where it performs best to improve energy efficiency, minimize energy cost, and minimize carbon footprint. The capacities of the electric heat pump and gas furnace are continuously adjusted based on ambient conditions, utility price signals, and marginal grid emission signals. An optimal model predictive control strategy was developed with the goal of minimizing utility cost and minimizing CO2 emission. Two case studies were conducted to simulate the performance of SFFHP during the heating season in Chicago and Los Angeles, respectively. Compared with a conventional electric heat pump, SFFHP yields 33% utility cost reduction and 49% CO2 emission reduction in Chicago. Similarly, it achieves 23% utility cost reduction and 17% CO2 emission reduction in Los Angeles. Case studies demonstrate that SFFHP can deliver significant reductions in peak demand, utility cost, and CO2 emission. Due to the hybrid fuel nature of this novel equipment, user comfort will always be maintained. The fuel flexibility makes it an attractive option for demand response programs.

Li, Zhenning↗