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DOE OSTI2022

A multi-scale time-series dataset of anthropogenic heat from buildings in Los Angeles County

The dataset contains hourly Anthropogenic heat (AH) from buildings in Los Angeles County, based on weather data from 2018. The hourly AH is aggregated at three spatial resolutions: 450m x 450m grid, 12km x 12km grid, and census tract. The AH is broken down into three components: building envelope surface convection, heating, ventilation, and air conditioning (HVAC) system heat release, and zone exfiltration and exhaust air heat loss. The dataset is created with the physics-based EnergyPlus building energy models to calculate individual buildings' AH considering WRF-UCM simulated microclimate conditions. Please refer to the paper "A multi-scale time-series dataset of anthropogenic heat from buildings in Los Angeles County" for more information about the data generation workflow and the data validation procedure. The data set contains two folders: the "output_data" folder holds the simulation results (EP_output and EP_output_csv), building metadata (building_metadata.geojson and building_metadata.csv), aggregated heat emission and energy consumption time-series data (hourly_heat_energy), and geographical data (geo_data) associated with the GEOID referenced in heat and energy consumption data. The "input_data" folder contains the raw data used to generate files in the "output_data" folder as well as data sets used in the validation. The code repository (https://github.com/IMMM-SFA/xu_etal_2022_sdata) holds the processing scripts for data curation, validation, and visualization.

Energy↗

DOE OSTI2024

Advanced Frost Sensor for HVAC Applications

Frost is a common problem in heating, ventilation, and air conditioning (HVAC) systems that can degrade their efficiencies, leading to excessive electricity consumption. Appropriate defrosting is therefore essential for heat pumps and refrigeration systems. However, these systems typically run the defrost cycle based on a predetermined time interval, a method that does not accurately identify the amount of frost and thus consume excessive electricity. To substantially improve defrosting initiation and termination, this paper describes development of a smart sensor based on a capacitive sensing technique that can quantify frost accumulation and difference between frost, ice, and water. The reported sensor adopts interdigitated comb electrodes and can be updated to a potential multifunctional sensor array for the enhancing identification of frost, ice, and water with the integrated measurement of surface temperature, frost capacitance, and resistance. The proposed frost sensor is potentially expected to have widespread applications in HVAC systems, including heat pumps, refrigerators, and commercial refrigeration systems.

Gao, Zhiming↗

DOE OSTI2025

NLR HPC Facility Power Usage Effectiveness (PUE) Data

Timeseries of Energy Systems Integration Facility (ESIF) Data Center Power Usage Effectiveness (PUE) Data provided in Parquet and compressed CSV formats Power Metrics Timeseries Fields: ts: Timestamp cooling_kw: Cooling (kilowatts) - Captures the power used by fans and pipe trace heaters associated with outdoor cooling equipment. The dedicated tower filter pump power is also captured as cooling load. energy_reuse: Energy Reuse Effectiveness hvac_kw: Heating, ventilation, and air conditioning (kilowatts) - Captures fan walls, fan coils that support the data center electrical rooms, and the make-up air unit. it_power_kw: IT equipment (kilowatts) - Captures power used by the IT equipment on the data center floor. plug_and_light_kw: Lights and utility plugs (kilowatts) - Captures power associated with the data center and dedicated mechanical room. The crank-case heater for the emergency standby generator is also captured as light and plug load. pue: Power Usage Effectiveness pump_kw: Pumps (kilowatts) - Captures power from pumps that move water in the data center Energy Recover Water loop and the Tower Water loops, and also captures power used by the boost pumps that circulate water through the fan walls. Note: The tower filter pump runs constantly to filter water from the data center cooling tower system, so 2.67 kilowatts are attributed to this pump and that is not reflected in this data field. day: Day of month Outside Weather Station Timeseries Fields: ts: Timestamp outside_air_humidity: Outside air humidity - Relative humidity percent outside_air_temp: Outside air temperature - Degrees Fahrenheit day: Day of month More detail: High-Performance Computing Data Center Power Usage Effectiveness

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

RL-HEMS: Reinforcement Learning Based Home Energy Management System for HVAC Energy Optimization

Heating, ventilation, and air conditioning (HVAC) is one of the major energy consumers in the residential sector. It is important to be able to monitor and control the energy consumed to provide utility services such as load shaping while satisfying the comfort and economic constraints of the homeowner. The objective of this work is to create the optimal schedule for HVAC operation to reduce the cost while satisfying the home-owner and equipment’s constraints using a model-free Reinforcement Learning (RL)-based optimization. The specific goal is to find the right balance between reducing energy cost, consumption, and customer comfort level. Our research effort addresses this optimization problem using multiple components: the development of initial learning test-bed and implementation of RL techniques on a real home. This will enable the rapid evaluation of the RL techniques and provide an early baseline to train before implementation on site. The RL algorithm is designed to learn the energy use patterns and generate the optimized schedule for HVAC within an acceptable time-interval to satisfy the homeowner’s comfort and minimize the energy usage. Our preliminary results are promising and we have observed a 17% reduction in the total cost and a 15% reduction in the power utilization using our RL-based HVAC model—RL-HEMS.

Kotevska, Olivera↗

DOE OSTI2020

2019 ASHRAE Handbook: HVAC Applications - Chapter 37 - Energy Use and Management

Handbook is a highly-regarded engineers reference book for heating, ventilating, air conditioning, and energy engineers. Building energy and water use metrics were developed for 53 commercial and residential building types. Supporting narrative was drafted and provided along with multiple tables of building performance metrics for publication in this ASHRAE handbook. ORNL (T. Sharp) developed the building performance metrics in Tables 2, 3, and 4 and authored the narrative about them. T. Sharp also authored the section on "Benchmarking Water Use" and developed building water use metrics published in Table 5.

Sharp, Terry↗

DOE OSTI2020

Assessing and Reducing Plug and Process Loads in Office Buildings

Plug and process loads (PPLs) account for 33% of U.S. commercial building electricity consumption (McKenney et al. 2010). (See Figure 1.) Minimizing these loads is a significant challenge in the design and operation of an energy-efficient building. Lobato et al. (2011) and Lobato et al. (2012) define PPLs as energy loads that are not related to general lighting, heating, ventilation, cooling, and water heating, and that typically do not provide comfort to the occupants. The percentage of total building energy use from PPLs is increasing. According to the U.S. Department of Energy (DOE), by 2030, commercial building energy consumption is expected to increase by 24%; PPL energy consumption is anticipated to increase by 49% in the same time frame (DOE 2010). These trends illustrate the importance of PPL energy reduction to achieve an overall goal of reducing whole-building energy consumption.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

Assessing and Reducing Plug and Process Loads in Retail Buildings

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

Optimizing HVAC Operations in Multi-unit Buildings for Grid Demand Response

The thermal inertia of buildings, along with the flexibility associated with thermostatically controlled loads (TCL) allows heating ventilation and cooling (HVAC) systems to be used for grid demand response (DR). In this work, we consider an HVAC system that serves multiple units in a residential building to meet their space heating requirements. We aim to determine the optimal power flow to each unit that minimizes the power costs incurred by the building's occupants while keeping in consideration their thermal comfort. The DR program is assumed to allow the building temperatures to deviate from the set-points up to a maximum limit. Despite the complex, non-linear structure of the problem, we show how the optimal solutions can be obtained efficiently using quadratic programming. Since HVAC systems can run on either electricity or natural gas, we study the efficacy of the DR regime for both hourly electricity prices and flat gas prices over the course of 24 hours. Finally, we run simulations to determine the optimal thermal power and the evolution of unit temperatures for various energy pricing schemes.

Naqvi, Syed A.↗

DOE OSTI2020

The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids: Preprint

The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. Different types of DERs such as Photovoltaics (PV), energy storage, electric vehicles (EVs), etc. have varying effects on the grid based on how and where they are deployed. Again, the DER deployment and its impact are network-dependent, while the traditional electric grids were not designed to host DERs. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper investigates the impacts of integrating a mix of DERs in the utility distribution network in Colorado, U.S. Firstly, BTM DERs at the residential scale including distributed PV; battery energy storage systems (BESS); heating, ventilation, and air-conditioning (HVAC) load; electric water heater (EWH) load; and EVs are modeled. The impacts of integrating these resources on the distribution network are evaluated by conducting time-series simulations for different scenarios considering different days to capture the worst-case conditions. Monte Carlo simulations are conducted to generate the realistic EV charging profile. The voltage issues, substation transformer loadings, and critical nodes in the network are identified with the incorporation of uncoordinated EV charging loads and other DER in the network. This analysis helps visualize the DER impact on the grid, identify the challenges, and provides an insight into the new distribution management and control needs to enable reliable and resilient distribution grid operations. Additionally, further analysis is performed to estimate the daily residential electricity cost with the inclusion of DERs under time-of-use tariffs. The result shows the daily residential electricity cost reduced by 20:7% on average with DERs compared to the case without DERs.

27 ARPA - Advanced Research Projects Agency-Energy↗

DOE OSTI2020

Energy Demand Management in Buildings Using PCM-Integrated Wall

The buildings in the United States annually consume nearly 40% of total U.S. energy consumption and approximately 75% of U.S. electricity consumption. Almost half of the total U.S. energy consumption in buildings is attributed to the space conditioning through Heating, Ventilation, and Air Conditioning (HVAC) systems. Utilizing thermal energy storage in buildings, particularly integrating phase change material (PCM) in the walls, has been reported to be an effective method of managing the space heating and cooling loads. This presentation covers an in-depth discussion on the integration of a PCM to building walls, the key conditions required for effective utilization of the PCM, and effect of the PCM transition temperature and the PCM location on energy-saving potential of the PCM-integrated wall.

building↗

DOE OSTI2020

Plowing Through the Cost Barrier: Zero Energy K-12 Schools for Less: Preprint

There is a perception that zero energy K–12 schools cost more than conventional schools. Zero energy schools provide a number of unique benefits to school districts, students and staff, and communities. Among other things, a zero energy school requires far less energy to operate than a conventional school and uses on-site renewable energy systems to offset that reduced energy load. The money saved on energy can often be used to enhance educational programs. But does the extra value come at a premium? Research findings indicate that not only can zero energy schools be designed and built on conventional school budgets, they can cost less. In an integrated design and construction process, the cost of zero energy measures can be offset by, for example, downsizing heating, ventilating, and air-conditioning systems, reducing both life cycle and first costs. These findings are based on an examination of 88 zero energy or zero energy ready schools across the United States built during the last 15 years. The data collected on these schools include capital costs and the experiences of owners and design teams. The goal was to understand the perceived cost barrier, given that each building was built with a predetermined budget. The results will help future school stakeholders, program administrators, and design teams counter the perceived cost barriers. Successful strategies for achieving zero energy at no initial cost are presented. The lessons learned from existing zero energy schools can help transform the market such that all new schools can be zero energy.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

Harvesting the low-hanging fruit of high energy savings -- Virtual Occupancy using Wi-Fi Data

Approximately 20% of primary energy consumed in the U.S. is attributed to HVAC use. Ideally, HVAC operation would be driven by actual building occupancy, but lack of reliable occupancy information often results in the use of conservative static schedules. This disparity is even more pronounced in a college campus, where the function of each space differs by building (classrooms, offices, libraries) and the class schedules change frequently -- every semester, day of week, and hour. While several research papers propose the use of counts of the Wi-Fi connections (e.g., phones, computers) as a proxy for occupancy, few real-world implementations exist. This paper describes the development and deployment of an open-source Wi-Fi-to-Occupancy software library in 65 buildings of a college campus, and the planned integration with the building energy management and control system at the building scale. Over a year of Wi-Fi data was gathered into distinct academic periods, including fall and spring semester, academic breaks, and summer sessions. Patterns such as students moving between classrooms, closing laptops before exams, etc., can be visualized from the data. Approximating occupancy from Wi-Fi data presents challenges which we address in this project -- for example, identifying static devices, or estimating the ratio of devices per person. Utilizing real-time occupancy data to inform optimal HVAC schedules and ventilation rates creates the potential to identify and reduce energy waste. Other potential applications include forecasting occupancy, and using Wi-Fi data to predict peak demands. Finally, the paper discusses how to easily scale these tools to other buildings.

Pritoni, Marco↗

DOE OSTI2020

Method and apparatus for reduction of water re-evaporation in a dedicated dehumidifier/water heater

An apparatus that includes a refrigeration circuit that includes an evaporator, a first condenser and a compressor. The apparatus includes a refrigerant-water heat exchanger that includes a second condenser fluidly coupled to the refrigeration circuit. A control valve is operatively connected to the refrigeration circuit to direct flow of refrigerant through at least one of the first condenser during a dehumidification mode and the second condenser during a water heating mode. A damper is disposed on an upwind side of the evaporator, the damper being operable to reduce airflow across the evaporator during a ventilation mode.

42 ENGINEERING↗

DOE OSTI2021

Long and Winding Road to Higher Efficiency-The RTU Story: Preprint

Rooftop units (RTUs) and other packaged heating, ventilating, and air-conditioning (HVAC) equipment consume more than four quads of energy annually while conditioning more than 50% of the commercial building floor area in the United States. Historically, these systems have low operating efficiencies and receive infrequent maintenance. In addition, the market has a low first cost, run-to-failure, like-for-like replacement mentality and has been slow to adopt change. This paper explores the broad market transformation that has resulted in higher efficiencies and tremendous energy savings. Although there are many factors in this market transformation, this paper highlights: research behind advancements in components and controls; adoption of an operational efficiency metric; raising the bar for high efficiency with the RTU Challenge; market barriers overcome through the Advanced RTU Campaign; upstream and midstream incentive programs and HVAC distribution networks; alignment of the market efficiency drivers of ASHRAE Standard 90.1, federal minimum efficiency standards, the ENERGY STAR® program, and the Consortium for Energy Efficiency's (CEE's) efficiency tiers. Measures of the market transformation include more than 50% increase in RTU efficiencies, more than 1 billion kWh saved and 160,000 RTUs upgraded with high-efficiency measures by the Advanced RTU Campaign partners, large increases of high-efficiency RTUs purchased through incentive programs, and the largest energy savings from any federal minimum standards action. Although these results are impressive, the market transformation is just beginning, and there remain exciting opportunities for improvements in equipment efficiencies and improved operating performance with advanced controls and fault detection and diagnostics.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

Adaptive Control for Residential HVAC Systems to Support Grid Services

In this paper, we propose an adaptive control strategy for residential buildings' heating ventilation and air conditioning (HVAC) systems to support grid services. The proposed control strategy captures the time-varying characteristics of the building and weather environments. It assigns to each building a local controller to maintain the indoor temperature within a desired comfort level, while the load aggregator allocates the assigned reference power provided by the distribution system operator to support a specific grid service, such as demand peak reduction, balancing supply and demand, and consuming the solar photovoltaic (PV) power locally. Simulation results are presented to verify the performance of the proposed control strategy in terms of maintaining the indoor temperatures of several buildings within the desired comfort bands while tracking the reference power profile. Also, a comparison with a recently developed model-free control strategy is presented.

Wu, Tumin↗

DOE OSTI2021

Data Analysis Approach for Large Data Volumes in a Connected Community

Recent advancements within smart neighborhoods where utilities are enabling automatic control of appliances such as heating, ventilation, and air conditioning (HVAC) and water heater (WH) systems are providing new opportunities to minimize energy costs through reduced peak load. This requires systematic collection, storage, management, and in-memory processing of large volumes of streaming data for fast performance. In this paper, we propose a multi-tier layered IoT software framework that enables effective descriptive and predictive data analysis for understanding live operation of the neighborhood, fault identification, and future opportunities for further optimization of load curves. We then demonstrate how we achieve live situational awareness of the connected neighborhood through a suite of visualization components. Finally, we discuss a few analytic dashboards that address questions such as peak load reductions obtained due to optimization, customer preference for automatic control of appliances (do they override the automatic control of HVAC?, etc.). 1 1 This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Chinthavali, Supriya↗

DOE OSTI2021

Coordinated Optimal Control of PV Inverters and HVAC Loads in Distribution Systems

The increasing integration of distributed energy resources (DERs), such as photovoltaics (PVs) and smart buildings into distribution systems complicate power system operation and controls. This paper proposes a coordinated optimal control strategy for PV inverters and Heating, ventilation, and air conditioning (HVAC) loads in smart buildings to minimize the total network loss in a distribution system. For the HVAC units, we enforce minimum on and off time constraints to avoid frequent switching that can degrade the unit. The proposed control will dispatch optimal control signals of active and reactive power to PV inverters and on/off commands to HVAC units while maintaining the nodal voltage within a secure range and the temperature of HVAC units at a comfort level. The simulation results on a modified IEEE 33-node distribution system demonstrate that the proposed coordinated control scheme can reduce the network loss.

Pani, Naveen↗

DOE OSTI2021

Air Return Strategies and Airborne SARS-CoV-2

Dedicated ducted air return per zone for heating, ventilating, and air-conditioning (HVAC) systems is sometimes claimed to be a superior technique over plenum return strategy from the viewpoint of exposure reduction to airborne pathogens. While both the return strategies have advantages, there is limited evidence in the literature as to which strategy is superior merely from a standpoint of building vulnerability to airborne contaminants. We performed multizonal airborne contaminant dispersion modelling using CONTAM to simulate the overall building vulnerability to airborne SARS-CoV-2 aerosols when released in an office building and evaluated the two air return strategies. Results showed that for ducted returns, maintaining negative pressure in the release zone coupled with 100% outdoor air supply can greatly reduce overall building vulnerability. However, for a building maintained under a slight positive pressure and a recirculated air percentage as low as 31%, ducted returns do not necessarily outperform plenum returns in terms of overall building vulnerability to airborne pathogens. Building-specific details and factors that are not easily represented with multizone modelling are important, making general preferential statements for either strategy difficult to make. Insights from this study can guide new construction and retrofits of buildings both during and after the COVID-19 pandemic with the aim of safe re-occupancy of buildings while keeping the buildings resilient against potential future epidemics spread by airborne agents.

Shrestha, Prateek↗