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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A critical review of environmental assessment tools for sustainable urban design

Cities are responsible for the depletion of natural resources and agricultural lands, and 70% of global CO{sub 2} emissions. There are significant risks to cities from the impacts of climate change in addition to existing vulnerabilities, primarily because of rapid urbanization. Urban design and development are generally considered as the instrument to shape the future of the city and they determine the pattern of a city's resource usage and resilience to change, from climate or otherwise. Cities are inherently dynamic and require the participation and engagement of their diverse stakeholders for the effective management of change, which enables wider stakeholder involvement and buy-in at various stages of the development process. Sustainability assessment of urban design and development is increasingly being seen as indispensable for informed decision-making. A sustainability assessment tool also acts as a driver for the uptake of sustainable pathways by recognizing excellence through their rating system and by creating a market demand for sustainable products and processes. This research reviews six widely used sustainability assessment tools for urban design and development: BREEAM Communities, LEED-ND, CASBEE-UD, SBTool{sup PT}–UP, Pearl Community Rating System (PCRS) and GSAS/QSAS, to identify, compare and contrast the aim, structure, assessment methodology, scoring, weighting and suitability for application in different geographical contexts. Strengths and weaknesses of each tool are critically discussed. The study highlights the disparity in local and international contexts for global sustainability assessment tools. Despite their similarities in aim on environmental aspects, differences exist in the relative importance and share of mandatory vs optional indicators in both environmental and social dimensions. PCRS and GSAS/QSAS are new incarnations, but have widely varying shares of mandatory indicators, at 45.4% and 11.36% respectively, compared to 30% in BREEAM Community. Considerations of economic and cultural aspects are only marginal in the reviewed sustainability assessment tools. However, the newly developed sustainability assessment tools such as GSAS/QSAS and PCRS diverge from their predecessors in their consideration of cultural aspects. - Highlights: • Reviews six urban sustainability assessment methods: LEED-ND, BREEAM Communities, CASBEE-UD, SBTool{sup PT}-UP, PCRS, GSAS/QSAS. • Reviewed methods are biased more towards the environmental, followed by social and economic dimensions of sustainability. • Water issues are highlighted in the Middle East but natural hazards are emphasized only in CASBEE and BREEAM Communities. • SBTool{sup PT}-UP, the most recent of the groups puts more weight (7.32%) on cultural aspects. • Share of mandatory indicators is highest (45.4%) in the Pearl Community Rating System (PCRS)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Health impact assessment – A survey on quantifying tools

Integrating human health into prospective impact assessments is known to be challenging. This is true for both approaches: dedicated health impact assessments (HIA) as well as inclusion of health into more general impact assessments. Acknowledging the full range of participatory, qualitative, and quantitative approaches, this study focuses on the latter, especially on computational tools for quantitative health modelling. We conducted a survey among tool developers concerning the status quo of development and availability of such tools; experiences made with model usage in real-life situations; and priorities for further development. Responding toolmaker groups described 17 such tools, most of them being maintained and reported as ready for use and covering a wide range of topics, including risk & protective factors, exposures, policies, and health outcomes. In recent years, existing models have been improved and were applied in new ways, and completely new models emerged. There was high agreement among respondents on the need to further develop methods for assessment of inequalities and uncertainty. The contribution of quantitative modeling to health foresight would benefit from building joint strategies of further tool development, improving the visibility of quantitative tools and methods, and engaging continuously with actual and potential users. - Highlights: • A survey investigated computational tools for health impact quantification. • Formal evaluation of such tools has been rare. • Handling inequalities and uncertainties are priority areas for further development. • Health foresight would benefit from tool developers and users forming a community. • Joint development strategies across computational tools are needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Applications of XPS in the characterization of Battery materials

In this study, technological development requires reliable power sources where energy storage devices are emerging as a critical component. Wide range of energy storage devices, Redox-flow batteries (RFB), Lithium ion based batteries (LIB), and Lithium-sulfur (LSB) batteries are being developed for various applications ranging from grid-scale level storage to mobile electronics. Material complexities associated with these energy storage devices with unique electrochemistry are formidable challenge which needs to be address for transformative progress in this field. X-ray photoelectron spectroscopy (XPS) - a powerful surface analysis tool - has been widely used to study these energy storage materials because of its ability to identify, quantify and image the chemical distribution of redox active species. However, accessing the deeply buried solid-electrolyte interfaces (which dictates the performance of energy storage devices) has been a challenge in XPS usage. Herein we report our recent efforts to utilize the XPS to gain deep insight about these interfaces under realistic conditions with varying electrochemistry involving RFB, LIB and LSB.

25 ENERGY STORAGE↗
DOE OSTI2014

Occupancy schedules learning process through a data mining framework

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10 minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Model calibration of a variable refrigerant flow system with a dedicated outdoor air system: A case study

With increased use of variable refrigerant flow (VRF) systems in the U.S. building sector, interests in capability and rationality of various building energy modeling tools to simulate VRF systems are rising. This paper presents the detailed procedures for model calibration of a VRF system with a dedicated outdoor air system (DOAS) by comparing to detailed measured data from an occupancy emulated small office building. The building energy model is first developed based on as-built drawings, and building and system characteristics available. The whole building energy modeling tool used for the study is U.S. DOE’s EnergyPlus version 8.1. The initial model is, then, calibrated with the hourly measured data from the target building and VRF-DOAS system. In a detailed calibration procedures of the VRF-DOAS, the original EnergyPlus source code is modified to enable the modeling of the specific VRF-DOAS installed in the building. After a proper calibration during cooling and heating seasons, the VRF-DOAS model can reasonably predict the performance of the actual VRF-DOAS system based on the criteria from ASHRAE Guideline 14-2014. The calibration results show that hourly CV-RMSE and NMBE would be 15.7% and 3.8%, respectively, which is deemed to be calibrated. As a result, the whole-building energy usage after calibration of the VRF-DOAS model is 1.9% (78.8 kWh) lower than that of the measurements during comparison period.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Field test and simulation evaluation of variable refrigerant flow systems performance

Our study aims to compare the performance of a Variable Refrigerant Flow (VRF) system with a Roof Top Unit, (RTU) and a variable-air-volume (VAV) system through field tests and energy simulations. The field test was conducted in as similar conditions as possible between the two systems, such as the installation and operating environments of heating, the ventilation and air conditioning (HVAC) system, including internal heat gain and outdoor conditions, including buildings to compare the performance of the two systems accurately. A VRF system and RTU were installed at the test building located in Oak Ridge, Tennessee, in the USA. The same internal heat gain was generated at the same operating time of the two systems using lighting, electric heaters, and humidifiers inside the building. The HVAC system was alternately operated between cooling and heating operations to acquire energy performance data and to compare energy usage. Furthermore, an hourly building energy simulation model was developed with regard to the VRF system and RTU, and then the model was calibrated using actual measured data. Then, annual energy consumption of the two systems were compared and analyzed using the calibrated model. Moreover, additional analysis was conducted when the controlled discharge air temperature in the RTU was changed. The field test result showed that when energy consumptions of two systems were compared at the same outdoor conditions, using the weather-normalized model, the VRF system exhibited an energy reduction of approximately 17% during cooling operation and of approximately 74% during heating operations. A comparison on the annual energy consumption using simulations showed that the VRF system reduced energy consumption more than that of the RTU by 60%. Furthermore, when the discharge air temperature in the RTU was controlled according to the outdoor air temperature, energy consumption of the RTU was reduced by 6% in cooling operations and by 18% in heating operation. As a result, energy consumption of the VRF system was reduced by more than that of the RTU by 55% approximately.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Equation-based object-oriented modeling and simulation for data center cooling: A case study

© 2019 Elsevier B.V. Data center cooling accounts for about 1% of electricity usage in the United States. Computer models are pivotal in designing and operating energy-efficient cooling systems. Compared to conventional building performance simulation programs, the equation-based object-oriented modeling language Modelica is an emerging approach that can enable fast prototyping and dynamic simulation of cooling systems. In this case study, we first modeled the cooling and control systems of an actual data center located in Massachusetts using the open-source Modelica Buildings library, and then calibrated a baseline model based on measurement data. The simulation of the baseline model identified several operation-related issues in the cooling and control systems, such as degraded cooling coils, improper dead band in control settings, and simultaneous cooling and heating in air handlers. Afterwards, we used a sequential search technique as well as an optimization scheme to investigate the energy saving potentials for different energy efficiency measures aiming to address the abovementioned issues. Simulation results show potential energy savings up to 24% by resolving identified control-related issues and optimizing the supply air temperature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Predicting Home Thermal Dynamics Using a Reduced-Order Model and Automatied Real-Time Parameter Estimation

Although smart thermostats have increasingly provided homeowners with abundant operational data related to advanced HVAC control and energy usage management in homes, there is a lack of systematic frameworks that can utilize such data to generate actionable information for advanced home comfort system diagnosis and control. Recognizing that a home thermal model, which is capable of connecting weather and HVAC operational data, is crucial to this framework, this paper introduces a home thermal model that is built upon the standard RC (Resistor-Capacitor) approach and the one virtual envelope assumption to describe the thermal dynamics of a home. A parameter estimation scheme is also developed that enables automatic, sequential, and optimal estimation of the model parameters, i.e., the thermal properties, of a home. Finally, the home thermal model and its parameter estimation scheme are tested using data collected over 63 consecutive days from a test home. In the test, the length of data that can provide enough robustness for parameter estimation is investigated. It is found that 10 days of data are enough, although 20 or more days of data can provide more robust results. In addition, when the model is used to make 12-hour-ahead predictions of indoor temperature the resulting mean, maximum, and 95%-confidence-interval absolute are 0.31 °C, 1.72 °C, and 0.90 °C, respectively, if the model is trained using 10 consecutive days of data, and 0.27 °C, 1.19 °C, and 0.72 °C, respectively, if 20 consecutive days are used. Therefore, the proposed home thermal model requires only a modest amount of training data to yield fairly accurate prediction and is able to perform better with more data.

home thermal model, Building thermal dynamics, ind↗
DOE OSTI2023

Long-term carbon intensity reduction potential of K-12 school buildings in the United States

School buildings have a great potential for carbon emission reduction since their annual emission is about 72 million metric tons. Currently, more than 30% of school buildings were built before 1960 and are underperforming. To effectively reduce carbon emissions via school building retrofits, it is critical for policymakers to understand the carbon intensity reduction potential of retrofitting school buildings in different regions. Hence, this study develops a method to comprehensively assess the long-term carbon intensity reduction potential of aggregated commercial buildings on a county-by-county basis in the continental U.S. We apply this method to the K-12 school buildings including primary and secondary school buildings. Here, this paper predicts the carbon intensity reduction potential of K-12 school buildings with eight building retrofit measures from 2022 to 2050 in the continental U.S. The results reveal several interesting findings: (1) In the approximately 3,000 counties of the U.S. from 2022 to 2050, the carbon intensity reduction potential of retrofitting K-12 school buildings in each county ranges from 0.41 kg/m 2 to 40.00 kg/m 2 . (2) Even in the same climate zone, the trends of carbon intensity reduction potential from 2022 to 2050 are different depending on their electricity sources. For example, in a hot and humid climate zone, the carbon intensity reduction potential in Florida will decrease from 2044 to 2048. However, in Mississippi, the carbon intensity reduction potential from 2044 to 2046 will increase due to the termination of the nuclear energy usage. (3) Generally, reducing lighting power density leads to more carbon intensity reduction in most states, but it might not be applicable for states with high clean energy penetration, such as Washington.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Component-Level Analysis of Heating and Cooling Loads in the U.S. Residential Building Stock

The residential building sector accounts for a substantial portion of total energy consumption in the United States and offers a significant opportunity for energy reduction and decarbonization through improvements in energy efficiency. Heating and air conditioning are the primary contributors to residential energy usage and electricity system peak demand. However, due to the diversity of the housing stock and the complexity of factors affecting heating and cooling demand, identifying the relative contributions to heating and cooling loads poses challenges. To address this, we applied the ResStock analysis tool to simulate 550,000 building energy models, providing statistical representation of residential buildings in the contiguous United States. We introduced outputs that quantified the heating and cooling influence of different components of a home, such as air leakage, envelope components (ceilings, walls, windows, foundations), internal heat gains from people, lighting, plug loads, and duct losses and gains. Leveraging the granularity of ResStock, we present a dataset to enable deeper understanding of the contributors to heating and cooling loads as a function of housing characteristics such as location, envelope efficiency, and building type. This work aims to support prioritization of research and development and informed decision-making for residential building decarbonization.

building simulation↗
DOE OSTI2024

The role of above-code labeling programs in reducing CO 2 e emissions in residential buildings

Residential buildings account for 15 % of total U.S. carbon dioxide (CO 2 ) emissions. Voluntary, above-code labeling programs, such as the ENERGY STAR and Zero Energy Ready Home programs, can play a crucial role in reducing the carbon footprint of the new construction residential building sector by encouraging adoption of more energy-efficient and lower emissions building practices and technologies. It's important for the industry to understand the carbon dioxide equivalent (CO 2 e) and energy cost savings potential of these programs. Here, to estimate the potential CO 2 e emissions savings for homes built to above-code labeling program levels, it is imperative to have reliable and accurate tools for estimating emissions from buildings. The open-source OpenStudio-ERI workflow employs detailed physics-based building energy modeling (BEM) to estimate energy usage and operational CO 2 e emissions. In this study we use the OpenStudio-ERI workflow and regional, time-varying emissions factors from the Cambium database to compare the CO 2 e emissions and energy costs of homes built to performance levels required by these above-code labeling programs relative to homes built to model energy codes. We present the results of our analysis and provide a range of emissions and annual energy cost savings for the above-code labeling programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Energy characteristics of multi-chiller load distribution algorithms in a large office building

Here, this study evaluates the energy efficiency of multi-chiller systems in large office buildings, focusing on their optimization across various climate zones as defined by ASHRAE. Using EnergyPlus for simulations, the research examines five different load distribution algorithms in multi-chiller systems that range from one to ten chillers, aiming to understand their effectiveness in 15 distinct climate zones. The primary objectives of the study include identifying the energy efficiency of multi-chiller systems in each climate zone, determining the appropriate number of chillers for each zone, and evaluating the performance of the load distribution algorithms. Based on the U.S. Department of Energy’s commercial building model, the results suggest that multi-chiller systems can significantly reduce cooling energy consumption in various climates. Among the algorithms evaluated, the Sequential Uniform Part Load Ratio (SUPLR) algorithm demonstrates notable efficiency, especially in the 4A climate zone (Baltimore), where it achieves substantial energy savings. Applying the SUPLR algorithm in a multi-chiller setup with four chillers in this zone leads to an estimated 24.5 % reduction in energy usage, equivalent to 183 MW annually. The research indicates that a range of 3 to 5 chillers is typically optimal for most climate zones. In-depth analysis in the 4A climate zone highlights the importance of minimizing operation hours at low Part Load Ratios (PLR) to ensure that chillers operate at a high Coefficient of Performance (COP). This strategy underscores the potential of well-designed multi-chiller systems to reduce cooling energy demand, particularly in climates with transitional seasons. This study provides an overview of the energy-saving potential of multi-chiller systems, applicable across a variety of climatic scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Benefits of Dual Fuel Heat Pump Grid-responsive Control: A Model-based Control Optimization Approach Using Building and Equipment Co-simulation

Conventional dual fuel heat pumps lack the intelligent control mechanisms to efficiently manage the switch between heat pump and furnace, leading to sub-optimal energy usage and, in some cases, increased operating costs. To resolve this gap, this study applies optimized control on hybrid heat pumps. With a focus on equipment control strategies, we compare the performances of five spacing heating equipment, including a conventional heat pump (HP), a conventional furnace, a dual fuel heat pump (DFHP) with conventional control, a dual fuel heat pump with smart control, and a novel seamlessly fuel flexible heat pump (SFFHP). While DFHP runs on either gas or electricity at any given moment, SFFHP concurrently consumes gas and electricity by continuously optimizing the proportion of each. In this research, a co-simulation framework is developed by integrating a building envelope model with a physics-based heat pump simulation model to analyze the benefits of grid-responsive controls of DFHP and SFFHP. The model-based optimal controls adjust the operation of the heat pump and gas furnace based on utility price signals and marginal grid emission to minimize utility cost and CO 2 emissions for multiple climate zones, different utility tariffs, and marginal grid emission scenarios. Case studies in Chicago and Los Angeles demonstrate that SFFHP and DFHP, with model-based optimal control, can deliver significant reductions in peak demand, utility cost, and CO 2 emission. In Chicago, SFFHP and smart controlled DFHP yield up to 64.7% and 61.7% utility cost reduction and up to 15.7% and 8.5% CO 2 emission reduction compared to the gas furnace. In Los Angeles, SFFHP and smart controlled DFHP achieve up to 43.6% and 40.1% utility cost reduction and up to 13.8% and 14.1% CO2 emission reduction compared to conventional heat pumps. In conclusion, by leveraging the fuel flexibility nature of dual fuel heat pumps, the model-based control optimization approach makes dual fuel heat pump an attractive option for demand response programs.

Control↗
DOE OSTI2019

Development of a topology analysis tool for fifth-generation district heating and cooling networks

Fifth generation district heating and cooling networks are characterized by supply temperatures in the ambient range of 15-25 degrees C, which not only reduces heat loss but also allows for integrating various kinds of low-temperature waste heat sources. The ability of these networks to absorb waste heat that is normally unrecoverable makes them an attractive solution for the future energy supply of urban areas. To enhance the adoption of these networks, this paper describes the development of a software tool to analyze the feasibility of fifth-generation of district heating and cooling systems in both new and existing districts. The research attempts to answer the question: 'Which buildings should be connected to a low-temperature hydraulic network given both the incremental benefits and costs relative to the scenario of decentralized (dedicated) heating and cooling systems for each building?' Therefore, all possible network layouts of the buildings are considered where the heating and cooling demands of each building are met by the fifth-generation of district heating and cooling network. For the heating and cooling load characterization of the buildings, reduced-order models are used while renewable energy and waste heat sources are included in the network. The focus of the research lies in the development of a hydraulic model for flexible use in the urban energy modeling and the optimization of the fifth-generation of district heating and cooling network topology for a given urban district. This research displays simulation results that quantify the performance of the fifth-generation of district heating and cooling network based on various output metrics, including primary energy usage, carbon dioxide emissions, and network implementation cost.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting

Here, the growing usage of decentralized renewable energy sources has made accurate estimation of their aggregated generation crucial for maintaining grid flexibility and reliability. However, the majority of distributed photovoltaic (PV) systems are behind-the-meter (BTM) and invisible to utilities, leading to three challenges in obtaining an accurate forecast of their aggregated output. Firstly, traditional centralized prediction algorithms used in previous studies may not be appropriate due to privacy concerns. There is therefore a need for decentralized forecasting methods, such as federated learning (FL), to protect privacy. Secondly, there has been no comparison between localized, centralized, and decentralized forecasting methods for BTM PV production, and the trade-off between prediction accuracy and privacy has not been explored. Lastly, the computational time of data-driven prediction algorithms has not been examined. This article presents a FL power forecasting method for PVs, which uses federated learning as a decentralized collaborative modeling approach to train a single model on data from multiple BTM sites. The machine learning network used to design this FL-based BTM PV forecasting model is a multi-layered perceptron, which ensures privacy and security of the data. Comparing the suggested FL forecasting model to non-private centralized and entirely private localized models revealed that it has a high level of accuracy, with an RMSE that is 18.17% lower than localized models and 9.9% higher than centralized models.

14 SOLAR ENERGY↗
DOE OSTI2025

Effects of ambient temperature on electric vehicle range considering battery Performance, powertrain Efficiency, and HVAC load

Here, this study investigates the impact of ambient temperature on the range of electric vehicles (EVs) by analyzing its effects on usable battery energy (UBE), heating, ventilation, and air conditioning (HVAC) energy consumption, and powertrain energy losses. Chassis dynamometer tests within a thermal chamber were conducted under various temperature conditions to investigate these impacts. The results indicate that lower temperatures lead to a decrease in UBE for lithium-ion batteries in EVs. At −18 °C, the UBE exhibited reductions of 4---8 % compared to the UBE at 22 °C. Battery thermal management strategies significantly affected the UBE loss, with different strategies resulting in distinct UBE reductions. HVAC energy consumption, especially for interior heating, proved to be the most dominant variable affecting EV driving range. Larger discrepancies between the HVAC target temperature (22 °C) and the ambient temperature increased HVAC energy usage. The type of HVAC system also influenced energy consumption, where EVs equipped with heat pumps demonstrated lower energy consumption for heating compared to those relying solely on resistance heaters. Ambient temperature also influenced motor energy consumption due to increased frictions, powertrain losses and tire rolling resistance at lower temperatures; consequently, regenerative braking energy decreased in cold conditions. Combining these effects influenced the overall energy consumption and driving range of EVs. At −18 °C, the driving range saw a substantial decrease of up to 60 % compared to 22 °C, while a slight decrease was observed at 35 °C.

Ambient Temperature↗
DOE OSTI2016

Dynamic performance analysis of two regional Nuclear Hybrid Energy Systems

In support of more efficient utilization of clean energy generation sources, including renewable and nuclear options, hybrid energy systems (HES) can be designed and operated as flexible energy resources (FER) to meet both electrical and thermal energy needs in the electric grid and industrial sectors. These conceptual systems could effectively and economically be utilized, for example, to manage the increasing levels of dynamic variability and uncertainty introduced by variable energy resources (VER) such as renewable sources (e.g., wind, solar), distributed energy resources, demand response schemes, and modern energy demands (e.g., electric vehicles) with their ever changing usage patterns. HES typically integrate multiple energy inputs (e.g., nuclear and renewable generation) and multiple energy outputs (e.g., electricity, gasoline, fresh water) using complementary energy conversion processes. This work reports a preliminary dynamic analysis of two realistic HES including a nuclear reactor as the main baseload heat generator and to assess the local (e.g., HES owners) and system (e.g., the electric grid) benefits attainable by their application in scenarios with multiple commodity production and high renewable penetration. It is performed for regional cases - not generic examples - based on available resources, existing infrastructure, and markets within the selected regions. This study also briefly addresses the computational capabilities developed to conduct such analyses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Evaluation of cooling setpoint setback savings in commercial buildings using electricity and exterior temperature time series data

Commercial buildings account for a significant amount of total energy produced in the US, and the Heating Ventilation and Cooling (HVAC) systems are one of the most significant components of their overall consumption. In this study, we proposed a new data-driven approach to evaluate HVAC cooling systems in commercial buildings and identify savings opportunities. The focus is an investigation of the impact of thermostat setpoint setback but using only whole building, electricity data taken at 15-min intervals for the analysis. We conducted a comparative study of setpoint setback characteristics on 432 commercial buildings with 5 building usage types across the United States. To accomplish this, both piecewise and Random Forest regression algorithms were employed using electricity and exterior temperature datasets to identify operational characteristics and the effective setpoints in the building to determine the corresponding savings opportunities. Both occupied and unoccupied time periods were studied across cooling degree days (CDD), when air conditioning is typically operational. Here the results show that in commercial buildings, on average, cooling systems account for 9.5% of total consumption. When a one degree setback during the cooling season is applied, an average of approximately 1.1% of annual consumption is achieved; retail and office buildings demonstrate the highest potential for savings. Additionally, we identified that the number of cooling degree days and base to peak ratio (BPR) are the most important variables for predicting the magnitude of the consumption of cooling systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗