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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Development and Evaluation of Occupancy-Aware HVAC Control for Residential Building Energy Efficiency and Occupant Comfort

Occupancy-aware heating, ventilation, and air conditioning (HVAC) control offers the opportunity to reduce energy use without sacrificing thermal comfort. Residential HVAC systems often use manually-adjusted or constant setpoint temperatures, which heat and cool the house regardless of whether it is needed. By incorporating occupancy-awareness into HVAC control, heating and cooling can be used for only those time periods it is needed. Yet, bringing this technology to fruition is dependent on accurately predicting occupancy. Non-probabilistic prediction models offer an opportunity to use collected occupancy data to predict future occupancy profiles. Smart devices, such as a connected thermostat, which already include occupancy sensors, can be used to provide a continually growing collection of data that can then be harnessed for short-term occupancy prediction by compiling and creating a binary occupancy prediction. Real occupancy data from six homes located in Colorado is analyzed and investigated using this occupancy prediction model. Results show that non-probabilistic occupancy models in combination with occupancy sensors can be combined to provide a hybrid HVAC control with savings on average of 5.0% and without degradation of thermal comfort. Model predictive control provides further opportunities, with the ability to adjust the relative importance between thermal comfort and energy savings to achieve savings between 1% and 13.3% depending on the relative weighting between thermal comfort and energy savings. In all cases, occupancy prediction allows the opportunity for a more intelligent and optimized strategy to residential HVAC control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Adaptive behavior and different thermal experiences of real people: A Bayesian neural network approach to thermal preference prediction and classification

Various observed and unquantifiable factors affect the thermal comfort of occupants in indoor environments and can lead to high uncertainty in the prediction and classification of their thermal preferences. The behavioral adaptation of occupants, by operating window systems for example, changes their thermal experience and expectations and therefore contributes to even higher prediction uncertainty. In this study, we applied a Bayesian neural network (BNN) algorithm to build a predictive model for occupant thermal preference using the ASHRAE Global Thermal Comfort Database II. The Bayesian method allows us to synthesize prior knowledge and available measurements into a unified modeling framework. It also offers a way to express and quantify uncertainty. Here we have performed a systematic study to test the efficiency and robustness of different BNN model configurations. In this study, the results show that the BNN model outperforms conventional thermal comfort models such as Predicted Mean Vote (PMV) and adaptive comfort model. The BNN model tends to produce more confident “prefer cooler” predictions with high possibility and low uncertainty. In contrast, the BNN model produces less certain predictions for “prefer no change” and “prefer warmer” across all occupants. Our findings suggest that linking occupants’ subjective evaluation measures and window opening/closing behavior to thermal comfort modeling effectively improves predictive performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Leading in Los Angeles: Demonstrating scalable emerging energy efficient technologies for integrated façade, lighting, and plug loads INTER System FLEXLAB Test Report

This report is a part of the California Energy Commission (Energy Commission) EPIC study Leading in Los Angeles: Demonstrating scalable emerging energy efficient technologies for integrated façade, lighting, and HVAC. The set of technologies are called the Integrated Technologies for Energy-efficient Retrofits (INTER) and are comprised of automated shading products and LED lighting systems with networked luminaire-level sensors and controls. In addition, the project will include control modifications and assessments of HVAC savings. This document includes the test plan and test results for the FLEXLAB® testing of the shading and lighting INTER system and related energy use impact. The test plan describes the test objectives and features, test cases, schedule, and measurements. The test results cover system performance in the lab; including lighting and HVAC energy, visual comfort, and thermal comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Sensor Impact Evaluation and Verification Technical Advisory Group Meeting Minutes

This report provides the technical advisory group meeting minutes and summary of detailed discussions for future development of sensor impact evaluations and verifications. Methods for sensor configuration/deployment have critical impacts on energy-efficient building control and thermal comfort. However, traditional sensor techniques for building operation and fault detection and diagnostics (FDD) are not optimal in terms of energy efficiency and thermal comfort, and their global effects are not thoroughly investigated. In an effort to address and overcome this limitation, a 3 year project, Sensor Impact Evaluation and Verification, was proposed. The multi-laboratory team—the US Department of Energy’s Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL), and the National Renewable Energy Laboratory (NREL)—is conducting early-stage R&D to provide technical supports and guidelines for sensor design in building/HVAC systems to optimize building energy use, FDD, thermal comfort, and grid efficiency. The overall goal of this project is to develop a framework that enables quantitative evaluation of the impact of sensors on building HVAC control, FDD, and consequently, building energy efficiency and thermal comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

THE CASE FOR MULTICRITERIA ANNUAL SUNLIGHT EXPOSURE GUIDELINES

This paper explored the effects of the current Annual Sunlight Exposure (ASE1000 lux, 250 hours) requirements on energy consumption and thermal comfort of an office space in five cities representing different U.S. climates. Specifically, the goal was to quantify the extent to which the current ASE metric requirements may be adjusted allowing for more/ less annual sunlight exposure to address climatic differences, energy, and thermal comfort criteria. A south-facing perimeter office space was simulated in the five climates to assess differences in energy and thermal comfort between the current ASE requirements and best-case scenarios. The results highlight the need to incorporate thermal comfort and energy as primary criteria informing target ASE requirements and guidelines. Lastly, the paper discusses research pathways to help develop multi-criteria ASE guidelines.

Abboushi, Belal K.↗
DOE OSTI2020

Design and analysis of optimal pre-cooling in residential buildings

Existing pre-cooling strategies provide a means of shifting or reducing the peak demand and/or energy cost in residential buildings. However, majority of them are rule-based and therefore may not be optimal in terms of cost saving, leaving room for improvement. In this paper, an integer linear programming problem that accounts for the thermal properties of a specific home, HVAC system capacity, utility rate structure, and weather conditions and makes use of a home thermal model is formulated. This problem determines the HVAC on/off control signal that minimizes the 24-h energy cost while maintaining thermal comfort and calculates the corresponding optimal indoor air temperature. The model is constructed using home thermal properties identified via data training in real-time. Through simulation, the energy performance of the proposed optimal pre-cooling strategy is investigated and compared with three rule-based operation strategies from the literature. It is found that the optimal strategy requires the least energy consumption without sacrificing thermal comfort. The superb energy performance of the optimal strategy is attributed to a longer runtime of the HVAC system in cool outdoor air conditions and to the elimination of deadband in HVAC operation, which is required by the rule-based strategies, to allow the indoor air temperature to stay near the thermal comfort upper bound as much as possible. In terms of energy cost, the rule-based operation strategies require 3.52, 1.90, and 2.79, respectively, while the optimal strategy only requires 1.52. These figures represent a saving of 56.82%, 20.00%, and 45.52%, respectively. The results suggest that the optimal strategy is indeed significantly more effective than the existing rule-based operation strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Long-Term field testing of the accuracy and HVAC energy savings potential of occupancy presence sensors in A Single-Family home

The energy-saving potential of occupancy-centric smart thermostats has been extensively explored in simulations but lacked field testing for energy savings quantification and sensor performance assessment in real buildings. This paper presents a long-term field study conducted in a single-family home in Texas, U.S. to evaluate the performance of occupancy-centric controls (OCC) of HVAC (heating, ventilation, and air-conditioning) system in terms of energy savings, sensor accuracy, and impact on electric peak demand. The test site was equipped with a commercial off-the-shelf (COTS) smart thermostat and multiple occupancy presence sensors for OCC implementation. Additionally, a sub-metering system was installed to monitor electricity consumption of various end-use equipment, including the HVAC system. A supplementary device was installed to track the ground-truth occupancy for the accuracy evaluation of the occupancy presence sensor. Scenarios of baseline and OCC controls were alternated weekly over the 20-month testing period. The results indicated an effective OCC execution, as evidenced by indoor temperature profiles. During the 2023 cooling season, OCC achieved total energy savings of 1,958 kWh, corresponding to a 17.6% energy savings ratio. Under certain conditions, daily HVAC energy savings reached as high as 17 kWh, with a savings ratio of 35%. Sensor performance showed an overall accuracy of 83.8%, a False Positive Rate (FPR) of 12.8%, and a False Negative Rate (FNR) of 47.4%. A key limitation was the sensor’s inability to detect stationary occupants during sleep, leading to a midnight FNR of nearly 100% and significantly compromising thermal comfort. Additionally, the implementation of OCC resulted in extended periods of high electricity demand on summer afternoons, affecting occupant’s thermal comfort and posing potential challenges to community-level grid operations if OCC were widely adopted. Furthermore, this study addresses a critical research gap by empirically investigating energy-saving potential and occupancy sensor performance in residential buildings. Through a comprehensive field-testing study, the research examines the interrelationship between sensor accuracy, energy savings, and thermal comfort, an area that has received limited attention in the current literature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Sensor Impacts Evaluation and Verification: Expert Interview Responses

The sensor configuration/deployment method has critical impacts on energy efficient building control and thermal comfort. However, traditional sensor techniques for building operation and fault detection and diagnostics (FDD) are not necessarily optimal in terms of energy efficiency and thermal comfort, and their global effects are not thoroughly investigated. In an effort to address and overcome this limitation, a multilaboratory and multiyear project, “Sensor Impact Evaluation and Verification,” was proposed. Its purpose is to develop a framework to investigate the impacts of sensor deployment and configuration on building energy optimization, FDD, occupant thermal comfort, and potentially grid efficiency. The first project task was a literature review to establish a solid knowledge of and a background related to sensor technologies and placement. To accomplish this task, an extensive review of previous research literature was performed. A series of expert interviews were conducted to augment the findings of the literature review. This report summarizes the interview design and interview results and findings. The interview was designed and performed to (1) investigate the current status and limitations of sensor configuration, (2) identify the research gaps and expectations for potential improvements in sensor configuration and deployment, and (3) integrate expert (e.g., researcher, building operation practitioner) knowledge and experience to develop use-case scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Thermoelectric air conditioning undergarment for personal thermal management and HVAC energy saving

Personal thermal management systems (PTMS) are highly desirable for improving individual thermal comfort and reducing indoor HVAC energy consumption. Although there have been many attempts in developing PTMS, existing PTMS are generally bulky, heavy or immobile. Besides, previous analysis on additional personal cooling and heating power required for maintaining thermal comfort in changing environments did not consider the individual difference in metabolism and the variability of comfort skin temperature with environmental conditions. In this paper, we report on the development of a novel lightweight (<1 kg) thermoelectric air conditioning undergarment system with a preferred design of branching tubing network (branching angle of 60° and diameter ratio of 0.782) for air distribution. Through thermal manikin and human subject tests, we demonstrated that the novel system is capable of providing a maximum of 15.5 W of personal cooling and 18.1 W personal heating with a coefficient of performance (COP) greater than 0.4, which is sufficient to the expansion of the indoor set-points by at least 2.2 °C (4 °F) on both sides without compromising thermal comfort and the potential saving of about 15% HVAC energy. Furthermore, our study improved the understanding of the required additional personal cooling and heating power for changing environmental conditions, which are essential guidelines for the further development of PTMS.

42 ENGINEERING↗
NASA NTRS1998

Space Suit Thermal Dynamics

The present NASA space suit (the Shuttle EMU) is a self-contained environmental control system, providing life support, environmental protection, earth-like mobility, and communications. This study considers the thermal dynamics of the space suit as they relate to astronaut thermal comfort control. A detailed dynamic lumped capacitance thermal model of the present space suit is used to analyze the thermal dynamics of the suit with observations verified using experimental and flight data. Prior to using the model to define performance characteristics and limitations for the space suit, the model is first evaluated and improved. This evaluation includes determining the effect of various model parameters on model performance and quantifying various temperature prediction errors in terms of heat transfer and heat storage. The observations from this study are being utilized in two future design efforts, automatic thermal comfort control design for the present space suit and design of future space suit systems for Space Station, Lunar, and Martian missions.

Campbell, Anthony B.↗
DOE OSTI2020

Development and Evaluation of Occupancy-Aware Model Predictive Control for Residential Building Energy Efficiency and Occupant Comfort

The residential sector accounts for 25% of global primary energy consumption. Two methods have previously been proposed to reduce residential energy use associated with the provision of occupant thermal comfort: 1. Occupancy-based HVAC control, operating systems only during confirmed occupancy, and 2. model predictive control (MPC), harnessing a mathematical model and forecasts to find optimal operating strategies. Previous studies estimate the average energy savings of the two methods individually in the range of 21% and 16%, respectively. The research presented herein was carried out to evaluate the energy savings potential in residential buildings by combining both approaches across different climates, house vintages, and occupancy patterns. Occupancy and eight different physical modalities (e.g. CO2 and VOC) data were collected from five homes for time periods of 4–9 weeks. Collected data sets were used to train occupancy prediction models suggested by an extensive literature survey of occupancy model types. The trained prediction models were combined with MPC and detailed EnergyPlus building simulation models to evaluate residential building performance in terms of annual energy savings and thermal comfort, along with discomfort exceedance metrics. Multiple home types and regions were analyzed to understand regional and climate-dependent potential. Based on actual field data, the occupancy models had a prediction inaccuracy between 8% and 35% across the investigated homes. Average occupancy for the collected data ranged from 56% to 86%, a typical range reported in the literature. Building simulations were conducted for three control scenarios: conventional thermostatic control, occupancy-based, and occupancy-based MPC. The results indicate that all advanced strategies improve upon the conventional control, with some scenarios cutting energy use in half with only occasional incurrence of discomfort. The findings indicate that occupancy-aware model predictive residential building control has the potential to drastically reduce energy use and associated emissions while maintaining occupant comfort for both new and existing buildings.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2021

Cabin Thermal Management Analysis for SuperTruck II Next-Generation Hybrid Electric Truck Design

In this article, we present a multistage, coupled thermal management simulation approach, informed by physical testing where available, to aid design decisions for PACCAR's SuperTruck II hybrid truck cabin concept. Focus areas include cabin insulation, battery sizing, and sleeper curtain position, as well as heating, ventilating, and air-conditioning (HVAC) component and accessory configurations, to maintain or improve thermal comfort while saving energy. The authors analyzed weather data and determined the national vehicle miles traveled weighted temperature and solar conditions for long-haul trucks. Example weather day profiles were selected to approximate the 5th and 95th percentile weighted conditions. A daylong drive cycle was developed to impose appropriate external wind conditions during rest and driving periods. Using the National Renewable Energy Laboratory's vehicle HVAC modeling and simulation tool VTCab, HVAC load design trade-off studies for the new truck geometry concept were completed. Parameters analyzed included effects of paint color, insulation, glass transmissivity, and curtain location. Simulation results helped with early design material selections for efficient cabin climate control. A detailed three-dimensional computer-aided engineering (CAE), computational fluid dynamics (CFD), radiation, and human physiology co-simulation, referred to in this article as CAE Thermal-CFD, was used to evaluate thermal comfort and energy impacts of diffuser configurations and air supply settings in driving and hoteling modes. Analysis revealed that it is more difficult to heat the cabin in hoteling mode during the winter than to cool the space in the summer. This seasonal load profile drives the requirement of additional energy storage for heating comfort. To determine the battery capacity requirement, multiday HVAC operation drive cycle simulations were then completed, showing that a 15-kWh battery would be required for HVAC operation during hoteling. Results helped reduce cabin thermal loads, determine component sizing requirements, and improve occupant comfort to save fuel and contribute to the economic viability of the hybrid system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Optimal electric-distribution-grid planning considering the demand-side flexibility of thermal building systems for a test case in Singapore

The planning of district-scale electric grids, i.e., distribution grids, has traditionally relied on finding the most cost-effective design such that they are able to supply the peak loads in a district. With the advent of electric demand side flexibility (DSF), there is the opportunity to reshape peak loads such that the investment cost of the electric grid decreases in exchange for a minor increase in the operation cost. This paper formulates an optimal planning approach for the electric grid at the district scale, which incorporates the DSF from thermal building systems, e.g., heating ventilation and air-conditioning (HVAC) systems. The problem is formulated as a mixed-integer linear program (MILP) and aims at minimizing the investment cost for the grid along with the operation cost of the flexible loads. This is subjected to the fixed electricity demand and thermal comfort constraints of building occupants. To this end, linear models for the thermal comfort in the buildings and the power flow in electric grid are considered. The approach is tested on a district planning test case based in Singapore, where the results show up to 30.9 % reductions in investment cost and up to 3.7 % reduction in total annualized cost. Urban planning authorities, developers and utility companies can all benefit from the presented approach to make optimized investment decisions. For building operators, the results point to the need of adopting their control systems for DSF.

Troitzsch, Sebastian↗
DOE OSTI2022

Development of a water source heat pump hardware-in-the-loop (HIL) testing facility for smart building applications

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while maintaining the thermal comfort of building occupants. As the pivotalpoint between building electricity consumption and indoor thermal comfort, high-efficiency electrical heatpumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on the heatpump operation andperformance under different weather, building load, and grid requests calls for investigation and verification via experimental tests with actual heat pumps integrated with real-time building and grid responses. This study presents a Water-Source Heat Pump Hardware-in-The-Loop (HIL) Test Facility developed with the capability to perform such tests. The hardware configuration for this testfacility introduces a hydronic system that emulates the conditions for the heat pump water-side, and a duct system that emulates conditions for the heat pump airside. Both data acquisition and emulator control are implemented through the National Instruments (NI) LabVIEW software running on an NI PXIplatform. The HIL mechanism based on the hardware-software integration that allows the testbed to communicate with a generic simulation environment is also discussed. Currently, the test facility setup includes a single heatpump and virtual building model in EnergyPlus coupled with an occupant behavioral model in MATLAB. Preliminary test results of the current setup demonstrate the building load emulator's ability to track the simulated gone temperature with a Root Mean Square Deviation (RSME) below 0.12°C (0.216°F). An uncertainty analysis based on sensor accuracies shows that the heat pump coefficient of performance (COP) can be measured with a relative uncertainty of 10.4% in cooling and 3.7% in heating. Apartfrom the current testing on a single heat pump, the test facility also provides the flexibility to include additional heat pumps to form a heat pump cluster, as well as coupling the heat pump with active thermal storage to provide enhanced demandflexibility.

Calfa, Caleb↗
DOE OSTI2023

Performance assessment of a real water source heat pump within a hardware-in-the-loop (HIL) testing environment

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while also maintaining the thermal comfort of building occupants. As the pivotal point between building electricity consumption and indoor thermal comfort, high-efficiency electrical vapor-compression heat pumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on heat pump operation and performance when subjected to different weather conditions, building loads, and grid requests calls for investigation and verification via experimental testing with actual heat pumps integrated with real-time building and grid responses. This study introduces a Water-Source Heat Pump (WSHP) Hardware-in-The-Loop (HIL) Test Facility that is the first of its kind. This testbed utilizes a 2-ton variable speed water-to-air heat pump that is capable of interacting with a virtual environment currently comprised of an EnergyPlus (E+) building simulation, an agent-based occupant behavioral model, and a single U-tube ground-loop heat exchanger (GLHE) model. Detailed descriptions of the testbed’s physical design and operation, virtual environment, as well as their mutual communication is provided. An uncertainty analysis is also performed under manufacturer specified heating and cooling design conditions. This analysis shows that the total load across the WSHP’s demand side heat exchanger, i.e., the sum of its latent and sensible components, can be measured with a relative uncertainty of ± 10.4% and ± 3.6% in cooling and heating mode respectively. The WSHP’s coefficient of performance (COP) can be measured with relative uncertainties of ± 10.4% in cooling mode, and ± 3.7% in heating mode. A preliminary 24-h experimental demonstration is then performed utilizing the DOE prototype small commercial office building model in E+. The simulation takes place in Atlanta, GA on the date of 08/26/15 from 12:00 AM to 11:59 PM using TMY3 weather data. Here, the results from this demonstration show that over the course of this experiment the simulated outputs of zone dry-bulb temperature, zone humidity ratio, and WSHP inlet water temperature can be tracked by testbed emulators up to a root mean squared error (RMSE) of ± 0.27 °C, ± 0.376 g/kg, and ± 0.85 °C respectively. The WSHP’s dynamic behavioral characteristics and performance are also captured, and correspond well with the authors’ previous understanding of heat pump efficiency as a function of evaporator and condenser fluid inlet conditions respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review

Due to the fast advancement of communication and information technology, intelligent buildings have garnered great interest. These buildings can forecast weather, ambient temperature, and sun irradiation and can modify heating, ventilation, and air conditioning (HVAC) operations appropriately, based on current and previous data. This change is intended to reduce HVAC system energy usage while maintaining an appropriate degree of thermal comfort and indoor air quality. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. Although substantial research has been conducted on MPC in building HVAC systems, there is a shortage of critical reviews and a lack of a comprehensive framework that formulates and defines the applications. Here, this article provides a comprehensive state-of-the-art overview of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, occupancy behavior, building type, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. The primary objective of this work is to highlight critical design characteristics for the MPC control scheme and to give improved suggestions for future research. Moreover, numerous prospective scenarios have been suggested that might provide future research direction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Evaluation of thermostat location for multizone commercial building performance

In multi-zone buildings, it is often found that a single shared thermostat controls more than one conditioned zones. Although these shared zones are supposed to have similar thermal needs (e.g., cooling and heating load), in reality, they are not mainly due to different orientations, sizes of windows, occupancy, space types, etc. This can cause unnecessary energy waste or thermal discomfort for the occupants. How to quantify this impact in multizone buildings remains a research gap. Therefore, this study aims to evaluate the impact of different sensor (i.e., thermostat) locations for multizone commercial buildings through a comprehensive modeling study. Here, two different scenarios for the sensor locations were selected to evaluate the impact in terms of energy and thermal comfort. The scenario (1) is that one to five sensors distributed among the five zones, but the sensor readings from selected zones will be used for no-sensor zones, which is no-mean sensor scenario. The scenario (2) is that one to five sensors distributed among the five zones, but the average temperature from the shared zones will be used for each of the shared zones, which is a mean sensor scenario. The uncertainty analysis was performed for different sensor location scenarios.(a)The major findings from an energy perspective, for scenario (1), the differences of cooling energy go as high as 17% more or 12% less, compared with the baseline. For heating energy consumption, the discrepancies go as high as 51% more or 52% less, compared with baseline. For site energy consumption, the discrepancies go as high as 3.2% more or 3.2% less, compared with baseline. For fan energy consumption, the discrepancies go as high as 3.2% more or as low as 1.0% less, compared with baseline. For scenario (2), the discrepancies of cooling energy go as high as 3% more, or 0.5% less, compared with the baseline. For heating energy consumption, the discrepancies, are go as high as 10.1% more or as low as 3.0% less, compared with baseline. For site energy consumption, the discrepancies go as high as 1.3% more or 0.3% less, compared with baseline. For fan energy consumption, the discrepancies go as high as 3.1% more or as low as 1.0% less, compared with baseline.(b) In terms of the indoor thermal comfort, for the no-mean-sensor scenarios, the discrepancies of unmet hours for cooling mode can be as high as 1,200 h, compared with the baseline. The discrepancies of unmet hours for heating mode can be as high as 740 h, compared with the baseline. For the mean-sensor scenarios, the discrepancies of unmet hours for cooling mode can be as high as 750 h, compared with the baseline. The discrepancies of unmet hours for heating mode can be as high as 50 h, compared with the baseline.

42 ENGINEERING↗