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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood

Heating, ventilation, and air conditioning (HVAC) systems account for the highest share of home energy consumption in the United States. Optimized HVAC control can provide thermal improved comfort to the occupants, improve energy efficiency, reduce energy cost, and support grid services. In this paper, we discuss a multi-agent and cloud-based software framework that has been deployed in occupied residential neighborhood. This system enables automatic data collection, learning, optimization, and dispatches signals to neighborhood devices. HVAC optimization is based on model predictive control (MPC). Since the operational performance of MPC depends on model forecasting accuracy, it is crucial to evaluate the model continuously and modify or retrain it as necessary. In this research, we developed an automated workflow to evaluate the performance of temperature and power forecasts based on measured data in the real world. This will provide researchers with a deeper understanding of the model and how it can be improved.

Lebakula, Viswadeep↗
DOE OSTI2023

Thermal Analysis of Liquid and Air Cooling of High-Power-Density Integrated Motor Drives

Thermal management is a crucial aspect in the design of motor and drive systems. This paper introduces a coordinated cooling approach utilizing both liquid and air to enhance the integrated motor drive's power density. The proposed solution incorporates a shared water jacket to cool both the machine and power electronics, combined with forced air ventilation for the end windings and rotor. This approach is structurally simpler, less expensive, and more effective than conventional non-interacting cooling schemes. The paper addresses the modeling of key thermal features and nonlinearities including material anisotropy, contact thermal resistance, and uneven winding loss distributions. In addition, this study utilizes a more detailed model of the loss distribution among the winding turns in each slot that delivers a higher level of accuracy. Finite Element Analysis (FEA) results demonstrate the effectiveness of the integrated cooling system for efficiently cooling the designed integrated motor drive system.

42 ENGINEERING↗
DOE OSTI2023

Integration of a Smart Outlet-Based Plug Load Management System with a Building Automation System

The growth of and reliance on renewable energy necessitate a multi-pronged approach to achieve grid reliability and economics. As they represent a notable portion of U.S. energy consumption, commercial buildings must play an active role in this effort. Conserving energy and responding to grid conditions through demand flexibility can be achieved through the integration of major building systems. Integration of plug and process loads with lighting and heating, ventilation, and air conditioning systems maximizes the effectiveness of integrated building energy management. In this research, we demonstrate the integration of smart outlets into a building automation system. We cover the installation process as well as the architecture required for smart outlets to communicate data to the building automation system and to receive commands back. After recording power measurements for one week as a baseline, we configured the building automation system to turn the smart outlets on and off according to a set schedule. This resulted in energy savings of 66% during 1 week on 25 plug loads. This work demonstrates that grid-interactive efficient buildings are achievable through building system integration.

building automation system↗
DOE OSTI2022

Evaluation of Equivalent Battery Model Representations for Thermostatically Controlled Loads in Commercial Buildings

Models for thermostatically controlled loads in commercial buildings often include many parameters and variables compared to residential buildings. As such, it is beneficial to use reduced-order models to represent these resources. A classic example of such a model is the Virtual Battery or Equivalent Battery Model (EBM). In this paper, the typical EBM is extended to higher-order commercial Heating, Ventilation, and Air-conditioning (HVAC) models and adapted for electric water heaters. Finally, we compare the performance of EBMs with detailed thermal models using three classic optimization problems - energy maximization, energy minimization, and power reference tracking. Our results show that the EBM-constrained and detailed thermal model-constrained problems produce similar outcomes in terms of temperature, power, and total energy consumption.

commercial buildings↗
DOE OSTI2019

Machine Learning-Based Investigation of the Associations between Residential Power Consumption and Weather Conditions

Machine learning feature selection analyses were conducted to understand the impact of various weather attributes on power electricity demand and its components in the northwest United States. Load profiles data were obtained from the Northwest Energy Efficiency Alliance (NEEA) and processed to yield hourly load profiles with the same temporal resolution as the weather conditions data obtained from the National Oceanic and Atmospheric Administration (NOAA) for representative weather stations. The data were divided into five seasons; for each season, for each load component, the influences of weather factors were evaluated and quantified using cross-correlation, principal component analysis, and mutual information evaluation; then predictive models were developed based on the ranked/screened factors using the regression tree (RT) approach. After multi-fold cross-validation, the optimal complexity/depth of the regress tree models is found to vary for different load components, and the prediction accuracy using RT and weather data can be higher than 80% for heating and refrigeration, or lower than 30% for vent and cooking. The study provides guidance on improvement of load profile generation, or even serves as a standalone predictive tool for approximating load profiles in balance authorities or climate zones where load profile data is absent.

load composite, load profiling, NEEA, mutual infor↗
DOE OSTI2019

Nuclear-norm-based Subspace Identification of Multi-Zone Building HVAC System

There have been many attempts to reduce the uncertainty associated with renewable energy. This paper aims to contribute to the field by considering the building heating, ventilation and air conditioning (HVAC) system, which is treated as flexible load. The control of the indoor temperature in the building employing the locally generated renewable energy has been carried out with both model-based and data-driven approaches. Although numerous efforts have been made in the area of construction of a model for the building HVAC system, almost all models have shortcomings. A building model that is locally accurate is required to be able to predict the load consumption in the future. This will benefit both the utility companies and building managers to estimate the upcoming load shape, especially the peak amount and duration. We consider the case of a multi-zone building HVAC system, i.e., each zone has its own thermostat and can be set to a different temperature. Based on our need for a multi-zone model that is locally accurate, we resort to the method of system identification. In particular, we will employ the nuclear norm based subspace identification method.

Telsang, Bhagyashri↗
DOE OSTI2024

Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning

Reinforcement learning (RL)-based methods have achieved significant success in managing grid-interactive efficient buildings (GEBs). However, RL does not carry intrinsic guarantees of constraint satisfaction, which may lead to severe safety consequences. Besides, in GEB control applications, most existing safe RL approaches rely only on the regularisation parameters in neural networks or penalty of rewards, which often encounter challenges with parameter tuning and lead to catastrophic constraint violations. To provide enforced safety guarantees in controlling GEBs, this paper designs a physics-inspired safe RL method whose decision-making is enhanced through safe interaction with the environment. Different energy resources in GEBs are optimally managed to minimize energy costs and maximize customer comfort. The proposed approach can achieve strict constraint guarantees based on prior knowledge of a set of developed hard steady-state rules. Simulations on the optimal management of GEBs, including heating, ventilation, and air conditioning (HVAC), solar photovoltaics, and energy storage systems, demonstrate the effectiveness of the proposed approach.

Huo, Xiang↗
DOE OSTI2023

Convergence Rate of Model Reference Adaptive Control with Application to Building HVAC Systems

Model reference adaptive control (MRAC) has been studied for decades and successfully applied in multiple areas, including heating, ventilation, and air conditioning (HVAC) systems for buildings. MRAC is efficient in capturing the time-varying characteristics of buildings' indoor temperatures and outdoor weather environments. In this paper, the rate of convergence of MRAC is investigated, where a direct adaptive control with temperature set point reference tracking is used to regulate the indoor temperatures for buildings. Numerical results show that by controlling the HVAC systems of residential buildings using MRAC, the indoor temperatures converge Q-sublinearly to the desired temperature set points. In addition, the rate of convergence for MRAC is compared with a baseline adaptive model-free control method.

Wu, Tumin↗
DOE OSTI2017

An Experimental Study of Energy Consumption in Buildings Providing Ancillary Services

Heating, ventilation, and air conditioning (HVAC) systems in commercial buildings can provide ancillary services (AS) to the power grid, but by providing AS their energy consumption may increase. This inefficiency is evaluated using round-trip efficiency (RTE), which is defined as the ratio between the decrease and the increase in the HVAC system's energy consumption compared to the baseline consumption as a result of providing AS. This paper evaluates the RTE of a 30,000 m2 commercial building providing AS. We propose two methods to estimate the HVAC system's settling time after an AS event based on temperature and the air flow measurements from the building. Experimental data gathered over a 4-month period are used to calculate the RTE for AS signals of various waveforms, magnitudes, durations, and polarities. The results indicate that the settling time estimation algorithm based on the air flow measurements obtains more accurate results compared to the temperature-based algorithm. Further, we study the impact of the AS signal shape parameters on the RTE and discuss the practical implications of our findings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Laboratory Demonstration of Variable-Speed Packaged Air-Conditioning Units for Provision of Frequency Regulation Services

Buildings are responsible for more than 73% of the total electricity use in the United States and one-third of the building electricity use is attributed to heating, ventilation and air-conditioning (HVAC) systems. If properly used, buildings could offer significant frequency control reserves to the power grid. This paper presents laboratory test results demonstrating the potential to use variable-speed packaged rooftop units for frequency regulation services. The test unit along with the proposed feedback control represents an add-on solution that requires minimum control upgrades to enable the provision of ancillary services. Test results show that HVAC systems can provide high-quality frequency regulation services with performance scores of up to 0.95. A first-order economic analysis also reveals significant economic benefits for building owners to participate in ancillary services.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

HVAC load Disaggregation using Low-resolution Smart Meter Data

Traditional non-intrusive load monitoring (NILM) methods are effective for load disaggregation using high resolution smart meter data collected by power quality meters. However, smart meter data collected and stored by utilities are normally 15-, 30- or 60-minute in granularity, making most NILM methods ineffective. This paper presents a novel sequential energy disaggregation (SED) algorithm for extracting heating, ventilation, and air conditioning (HVAC) energy consumptions from residential and small commercial building loads using 30 min smart meter data. Large, infrequently-used loads are first detected and removed from the total building energy consumption. Then, base energy consumption curves, defined as the energy consumption without heating and cooling loads, are identified using the mild-day method. After that, the heating and cooling loads are extracted using an average value subtracting method. Simulation results with real data show that the proposed SED method is computationally efficient, simple to implement and robust in performance across different types of buildings.

Liang, Ming↗
DOE OSTI2020

A Machine Learning-based Approach to Predict the Aggregate Flexibility of HVAC Systems

Use of renewable energy resources can play a significant role in mitigation of climate change since they reduce the dependency on fossil fuels. However, high penetration of renewable energy resources into the power grid may cause some reliability problems. Ancillary services are key to realize the potential of renewable resources without disturbing the grid reliability. In recent years, many control approaches focused on exploiting the aggregate flexible heating, ventilation, and air-conditioning (HVAC) loads of buildings. However, there are still research gaps in assessing in advance the resulting indoor temperature responses for given total power and weather profiles before implementing the controls in the buildings. Such an assessment is critical to guarantee satisfying both the grid related objective (reliability) and the building related objective (occupants' comfort). Towards addressing this research gap, this paper presents a machine-learning approach for predicting the ratings of indoor temperature responses. In this approach, a prediction model was developed using a dataset generated by a set of model-free control simulations. The developed model achieved high prediction accuracy and showed the potential of the proposed approach.

Amasyali, Kadir↗
DOE OSTI2020

Signal Temporal Logic Control for Residential HVAC Systems to Accommodate High Solar PV Penetration

This paper proposes a new signal temporal logic (STL) control for ON/OFF residential buildings' Heating Ventilation and Air Conditioning (HVAC) systems. STL is used to control indoor temperatures while consuming most of the generated solar photovoltaic (PV) power locally to minimize its impact on the grid and reduce the need for large energy storage devices. In contrast to most, if not all, control mechanisms such as the traditional model predictive control (MPC), STL control allows for including temporal constraints in the control formulation to further relax indoor temperatures' constraints and allow them to exceed the comfort band limits for a prespecified (short) period of time. This relaxation allows to consume an additional PV power by the HVAC systems, which prevents such an unwanted intermittent power from affecting the grid. We formulate the MPC-based STL control mechanism to implement the objective. Simulation results show that the PV tracking performance has been improved while employing the proposed STL controller.

Wu, Tumin↗
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 OSTI2019

Community Microgrid Planning Considering Building Thermal Dynamics

In this paper, the building thermal dynamic characteristics are introduced in the community microgrid (MG) planning model. The proposed planning model is formulated as a mixed integer linear programming (MILP) which seeks to determine the optimal deployment strategy for various distributed energy resources (DER). The objective is to minimize the annualized cost of community MG, including investment cost in DER, operation cost for dispatchable fuel-generators (DFG), energy storage system (ESS) degradation cost, energy purchasing and peak demand charge at PCC, customer discomfort cost due to the room temperature deviation and load curtailment cost. Given the slow thermal dynamic characteristics of buildings, the heating, ventilation and air-conditioning (HVAC) system in the proposed model is treated as a demand side management (DSM) component, whose dispatch commands are provided by the central MG controller. Numerical results based on a community MG comprising of 20 residential buildings demonstrate the effectiveness of the proposed planning model and the benefits of introducing the building thermal dynamic model.

Zhang, Xiaohu↗
DOE OSTI2012

WESBES: A Wireless Embedded Sensor for Improving Human Comfort Metrics using Temporospatially Correlated Data

When utilized properly, energy management systems (EMS) can offer significant energy savings by optimizing the efficiency of heating, ventilation, and air-conditioning (HVAC) systems. However, difficulty often arises due to the constraints imposed by the need to maintain an acceptable level of comfort for a building’s occupants. This challenge is compounded by the fact that human comfort is difficult to define in a measurable way. One way to address this problem is to provide a building manager with direct feedback from the building’s users. Still, this data is relative in nature, making it difficult to determine the actions that need to be taken, and while some useful comfort correlations have been devised, such as ASHRAE’s Predicted Mean Vote index, they are rules of thumb that do not connect individual feedback with direct, diverse feedback sensing. As they are a correlation, quantifying effects of climate, age of buildings and associated defects such as draftiness, are outside the realm of this correlation. Therefore, the contribution of this paper is the Wireless Embedded Smart Block for Environment Sensing (WESBES); an affordable wireless sensor platform that allows subjective human comfort data to be directly paired with temporospatially correlated objective sensor measurements for use in EMS. The described device offers a flexible research platform for analyzing the relationship between objective and subjective occupant feedback in order to formulate more meaningful measures of human comfort. It could also offer an affordable and expandable option for real world deployment in existing EMS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

Fuzzy Linguistic Knowledge Based Behavior Extraction for Building Energy Management Systems

Significant portion of world energy production is consumed by building Heating, Ventilation and Air Conditioning (HVAC) units. Thus along with occupant comfort, energy efficiency is also an important factor in HVAC control. Modern buildings use advanced Multiple Input Multiple Output (MIMO) control schemes to realize these goals. However, since the performance of HVAC units is dependent on many criteria including uncertainties in weather, number of occupants, and thermal state, the performance of current state of the art systems are sub-optimal. Furthermore, because of the large number of sensors in buildings, and the high frequency of data collection, large amount of information is available. Therefore, important behavior of buildings that compromise energy efficiency or occupant comfort is difficult to identify. This paper presents an easy to use and understandable framework for identifying such behavior. The presented framework uses human understandable knowledge-base to extract important behavior of buildings and present it to users via a graphical user interface. The presented framework was tested on a building in the Pacific Northwest and was shown to be able to identify important behavior that relates to energy efficiency and occupant comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A Critical Exploration of the Efficiency Impacts of Demand Response From HVAC in Commercial Buildings

Increasing quantities of renewable energy generation has yielded a need for greater energy storage capacity in power systems. Thermal storage in variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) in commercial buildings has been identified as an inexpensive source of grid storage, but the true costs are not known. Recent literature explores the inefficiency associated with providing grid services from these HVAC-based demand response (DR) resources by employing a battery analogy to calculate round-trip efficiency (RTE). Results vary significantly across studies and in some cases reported efficiencies are strikingly low. This article has three objectives to address these prior results. First, we synthesize and expand on insights into existing literature by systematically exploring the potential causes for the discrepancies in results. We reinforce previous work indicating baseline modeling may drive differences across studies and deduce that control accuracy plays a role in the major differences between experiments and simulation. Second, we discuss why the RTE metric is problematic for DR applications, discuss another proposed metric, additional energy consumption (AEC), and propose an extension, which we call uninstructed energy consumption (UEC), to evaluate DR performance. Finally, we explore the merits of different metrics using experimental data and highlight UEC's reduced sensitivity to the characteristics of the DR signal than previously proposed metrics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗