A CYBER-PHYSICAL SYSTEM (CPS) APPROACH TO ENABLING ENERGY-EFFICIENT, SUSTAINABLE BUILDINGS

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: HATCH
• Reporting Frequency: Annual
• Accession No.: 1008484
• Project Start Date: Nov 19, 2015
• Project End Date: Sep 30, 2020
• Program Code: [(N/A)]- (N/A)

Recipient Organization
OHIO STATE UNIVERSITY
1680 MADISON AVENUE
WOOSTER,OH 44691

Performing Department
Food, Agric and Biological Engineering

Non Technical Summary
This research will develop cognitive green building technologies to make a significant impact on the energy sustainability, environmental adaptability, and civil infrastructure reliability of future buildings. The proposed research will not only advance the knowledge in the design of cognitive green buildings but will also serve a critical need in the general Cyber-Physical System (CPS) research community by developing an analytical foundation and cross-cutting principles that will shed insight on the control and design of general CPS.The proposed research project will fit the U.S. Department of Agriculture's (USDA) new climate change goals by cutting down CO2 emissions associated with buildings, which also include agricultural buildings, food processing and storage facilities, and residential houses for farmers.Buildings in the U.S. account for approximately 40% of national energy use and 39% of total CO2 emissions. Minimizing buildings' energy consumption and environmental impacts is crucial to our nation's energy security while protecting our environment. Also, energy efficient buildings can help building owners reduce their operating costs, leading to a significant amount of savings during the life cycle of the buildings. Although many energy efficient (EE) building technologies have already been implemented in new construction and retrofits of existing buildings, the energy performance of these buildings is not necessarily improved to the anticipated levels.Currently, green building designs are typically organized around a set of energy consumption benchmarks, e.g., LEED rating system by the U.S. Green Building Council (USGBC), without taking a holistic CPS viewpoint. Such ad hoc "green designs" are myopic and do not fully take into account key interdependencies between subsystems and users, thus leading to undesirable solutions and outcomes. For example, lighting accounts for 17% of the electricity used by both commercial and residential buildings, and replacing traditional incandescent light bulbs with costly high-efficiency lighting (e.g.,CFL, LED, etc.) has become a standard practice for green energy design. However, these higher efficiency lights produce much less heat than traditional ones. Hence, an unexpected outcome is that in cold weather regions, designing buildings using high-efficiency lighting requires heating systems to run longer to compensate for this heat loss. As a result, these costly reductions in lighting energy are almost offset by increase in the space heating requirements.In reality, around 28-35% of LEED-certified buildings actually consumed more energy than their conventional counterparts, even though they have more efficient lighting and heating, ventilation, air conditioning (HVAC) systems. This highlights the importance of advanced building energy analytics and calls for new holistic and cognitive energy management approaches for green buildings.The proposed study aims to investigate and address building energy and comfort problems based on a cyber-physical system approach, which emphasizes building system integration, real-time sensing and communication, and advanced energy control algorithms. As pointed out earlier, myopic strategies have limited the effectiveness of energy conservation measures and may even incur unintended consequences. Thus, our goal is to develop holistic energy management schemes that exploit demand elasticity and adapt to: (i) occupancy patterns, (ii) human comfort zone, and (iii) ambient environments.This line of research is important for the building and facility management industry to provide truly energy efficient, comfort and healthy, and economically viable buildings.

Animal Health Component

20%

Research Effort Categories

Basic

80%

Applied

20%

Developmental

(N/A)

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
402	5399	2020	50%
404	5399	2020	50%

Knowledge Area
404 - Instrumentation and Control Systems; 402 - Engineering Systems and Equipment;

Subject Of Investigation
5399 - Structures, facilities, and equipment, general/other;

Field Of Science
2020 - Engineering;

Keywords

energy efficiency

building

cyber-physical system

thermal comfort

predictive analysis.

Goals / Objectives
The proposed study aims to develop a unified analytical approach for green building design and operation that comprehensively manages energy sustainability by taking into account the complex interactions between systems of systems and between buildings and power grid. The specific objectives are listed below:Intra-Building Integrated Energy Management: Develop novel cognitive control schemes that allow each building to opportunistically exploit the energy demand elasticity of the building subsystems and adapt to (i) occupancy patterns, (ii) human comfort zones, and (iii) ambient environments. This research thrust jointly considers the complex interactions among building subsystems (e.g., HVAC, lighting, appliances, etc.) and ambient building environments (seasonal climates, indoor-outdoor temperature difference, building thermal properties, human comfort zones, lighting intensity needs, etc.).Managing Multi-Building Interactions: Ensure that not only are the energy costs minimized, but also that there are no instabilities caused in the power grid due to myopic individual actions. This research thrust concentrates on energy management for green building-clusters by developing (near) optimal distributed control and coordination schemes that provide real-time performance guarantees. This research is particularly relevant for utility providers and organizations (e.g., universities, governments, etc.) that manage a wide spread of buildings and wish to control and minimize the overall energy and operational costs.

Project Methods
To achieve the energy efficiency goal of cognitive green buildings, we will begin by developing holistic energy control schemes for a single building and then move to a network of buildings in the context of smart grid.Cognitive Energy Analytics for Intra-Building Integrated Energy ManagementTo achieve the energy efficiency goal of cognitive green buildings, we will begin by developing holistic energy control schemes for a single building. The procedures are listed below:1. Create and validate a whole-building energy simulation model for the investigated building.A campus building, the New Dreese Laboratory (NDL), is selected as the case study building for this research, which alsoprovides a suitable test bed for developing an energy-efficient and occupant-satisfactory control algorithm. In previous work, more than 100 temperature sensor nodes were deployed in the whole building. These nodes compose a wireless sensor network, ThermoNet, which had been collecting the temperature and lighting data over a year since June 2010.In the proposed research project, a physical approach will be adopted to build a predictive model for the NDL building. The accuracy of simulation will be evaluated by comparing temperature distribution between simulation results and the historical data collected by ThermoNet, and model calibration will be performed to improve the accuracy of model prediction.In this project, we will adoptDesignBuilder (developed by DesignBuilder Software Ltd) as our simulation tool.2. Perform energy and thermal comfort analysis to identify potential building design and control problems.Once the model has been calibrated and validated, we will generate predictions for the building's indoor temperature and annual energy consumption. Building energy consumption will be analyzed based on end-use categories and benchmarking with the Commercial Buildings Energy Consumption Survey (CBECS), a national survey that records the energy consumption and expenditures data on commercial buildings. Through such analyses, energy usage abnormalities in this building will be identified for further research.This study will perform indoor thermal comfort analysis based on ANSI/ASHRAE Standard 55-2004.3. Develop advanced HVAC control strategies.In this project, we plan to leverage ourpreliminary results to fully investigate the following open research problems:Understanding fundamental limits of elastic energy saving with human comfort zone.In our preliminary study, we only used a preheating example to illustrate potential energy savings in occupancy adaptation. The opportunity in pre-cooling remains to be explored. Further, elasticity exists during normal operation hours. For example, rather than maintaining a fixed set point temperature, we can exploit our recent work on human comfort zone modelingand subsequently design an opportunistic HVAC maintaining scheme. All these approaches allow us to achieve energy saving far beyond that under existing occupancy-based control.Understanding spatial impact on building energy control. In practice, there could be multiple spatially distributed heating/cooling zones in a building, which could be independent or inter-connected. Moreover, a change of energy control decision in one zone may affect or interfere with the climate behavior (and thus energy control) in another zone. Therefore, one has to jointly consider such interdependency in spatial domain to make optimal energy control decisions. In this project, we will develop the necessary analytical models and theoretical results to understand spatial domain impacts on energy control.Exploiting statistical information of renewable energy in elasticity-based building energy minimization. In our preliminary approach, we do not assume knowledge of the underlying stochastic models of renewable energy sources a priori, and use the subgradient-based stochastic approximation (SG-SA) technique to design an online algorithm that converges to the underlying stochastic models implicitly. However, the SG-SA approach may not converge quickly if the subgradient step-sizes are not chosen wisely. On the other hand, we note that the performance of our elasticity-based building energy control could be significantly enhanced if accurate statistical models for renewable energy sources are available. When such stochastic models are available, we propose to employ techniques from stochastic programming in our elasticity-based building energy control.4. Predict building energy and thermal performance based on the calibrated model and newly developed HVAC control schemes. The energy modeling software we use equips with the ability to handle feedback from the HVAC system to the zone conditions. This feature gives researchers a broader field to explore and discuss the feedback control strategies for HVAC system. In this research, new prediction results will be generated based on the proposed HVAC control strategies and compared with initial predictions to assess the performance improvement in energy use and thermal comfort.Cognitive Energy Analytics for Green Building-Clusters In this section, we move one step further and consider energy control for green building-clusters to avoid such unintended consequences. This problem is particularly relevant for a utility company serving a large number of residential buildings, business organizations, universities, or governments that own campuses or a wide spread of buildings and wish to control and minimize their overall operating costs. By optimizing energy control of multiple buildings on a large scale, a green building-cluster could lead to significant cost savings and carbon footprint reductions for building owners.Encouraged by our preliminary results, we plan to leverage the proposed approaches to investigate the following important open problems:Interplay between utility provider and users on grid stability. So far, our energy control focus is from the perspective of an owner or utility company that owns or serves a cluster of buildings, based on which we design internal control signals (or virtual prices). However, it is also important to consider the relationship between the utility company and these clusters of networks, otherwise the energy loads that are generated by these clusters could cause large-scale fluctuations in energy demands in response to exploiting the lower exogenous prices from the electricity market. In our future works, we will study these various ways in which a utility company can coerce users into smoothing out the energy demands so as to prevent power grid instability.More complex environmental and social dynamics with multiple utility providers. So far, we only consider the case where the controller purchases electricity from a single utility company. The electricity market in reality is far more complex, with multiple utility companies competing, coordinating, or trading for customers. Analyzing the performance of different energy demand control polices and their resultant environment and sustainability impacts is still challenging, and the solutions of optimal energy control schemes remain elusive. In this project, we plan to extend and generalize our proposed distributed control algorithms from the single utility provider case to complex and large-scale social environments.The impact of uncertain inelastic building energy demands. In this project, we plan to investigate the impact of inelastic load uncertainties from two approaches. The first approach aims to study the ergodic energy cost minimization problems again by employing similar stochastic approximation techniques. The second approach will be centered on a deterministic model, where the inelastic energy demands are modeled as unknown random variables within a set with known mean and variance. Our goal is to design robust optimization algorithmsthat minimize the energy costs under the worst-case inelastic load scenarios.

Progress 11/19/15 to 09/30/20

Outputs
Target Audience:The target audiences include researchers in building energy and environment-related areas, professionals working in the architecture, engineering, construction, and facility management industry, building owners, and university students majoring in the related fields. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?In 2019, two undergraduate students were involved in developing green home virtual tours and updating website information. Due to the pandemic, we were not able to hold any workshops or travel to meetings and conferences during 2020. How have the results been disseminated to communities of interest?We distributed the research results through peer-reviewed journal publications. More practical information was disseminated through the GHTC web portal. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? At the final stage of this research project, we conducted extensive and in-depth simulations to verify the performance of our proposed mathematical model and algorithms for minimizing buildings' cooling energy costs and peak demand. The simulation results show that our optimization algorithms outperform existing HVAC cooling control strategies (including those state-of-the-art methods) examined in the research. We developed a journalarticle based on this simulation study and the paper has been accepted for publication. We also continued to enhanceour Green Home Technology Center (GHTC) education and outreach web portal by adding additional materials and a newly developed green home virtual tour. So far, this website has attracted more than 30,000 users. To carry on this effort, we continued to write educational grant proposals and received an Engagement Impact grant through which we will develop survey instruments to seek feedback from users,assess the impact of our program, andcreate a list of green home practitioners at GHTC as a valuable resource. This program will help improve market penetration of green home technologies and make a real impact on quality of lifeof Ohioans.

Publications

• Type: Journal Articles Status: Published Year Published: 2020 Citation: Shi, H., Liu, J., and Chen, Q. 2020, An RC-network approach for HVAC precooling optimization in buildings, IEEE Transactions on Sustainable Computing, 10.1109/TSUSC.2019.2943491.
• Type: Journal Articles Status: Accepted Year Published: 2021 Citation: Shi, H. and Chen, Q. 2021. Building energy management decision-making in the real world: A comparative study of HVAC cooling strategies, Journal of Building Engineering, 33(January), #101869, https://doi.org/10.1016/j.jobe.2020.101869.
• Type: Journal Articles Status: Under Review Year Published: 2021 Citation: Shao, H., Song, P., Mu, B., Tian, G., Chen, Q., He, R., and Kim, G. Assessing city-scale green roof development potential using unmanned aerial vehicle (UAV) imagery, Urban Forestry & Urban Greening, 2nd revision under review.

Progress 10/01/18 to 09/30/19

Outputs
Target Audience:The target audiences include researchers in building energy and environment-related areas, professionals working in the architecture, engineering, construction, and facility management industry, building owners, and university students majoring in the related fields. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?I supervised two undergraduate students to develop educational materialsfor green homes. Their knowledge in green home technologies was significantly improved, as well as their website andvideo development skills. How have the results been disseminated to communities of interest?In addition to high quality journal publications, we further developed and enhanced our extension website (greenhome.osu.edu). During the year of 2019, this website has attracted over 6,500 users with more than 12,000 views. What do you plan to do during the next reporting period to accomplish the goals?We plan to extend the RC-network based modeling to study energy sustainability at city scale.

Impacts
What was accomplished under these goals? In the past year, we conducted extensive numerical simulation studies to examine the effects of both internal building parameters and external environments on the performance of our proposed precooling optimization algorithm. First, we examined the effects of five building parameters on the performance of the optimal strategy based on the cooling energy cost reduction ratio. These parameters include i) the gross floor area, ii) average room size, iii) total number of rooms, iv) wall capacitance, and v) the window-to-wall ratio. For each combination of parameter values, we randomly generated 10 building samples for daily simulation. The total number of samples is 1,640. Then, we examined the effects of the internal loads on the performance of the optimal strategy compared with the baseline case of ON/OFF strategy. The building samples with 20 rooms are simulated with different internal load ratios (i.e., 0.125, 0.5, 0.75, and 1). The internal load ratios less than 1 represent buildings with less plug loads or at various occupancy levels. Finally, we examined the effects of the external environments on the performance of the optimal strategy against the ON/OFF baseline case. Besides Columbus, which has been simulated earlier in our study, the building samples with 20 rooms are simulated in the other four cities. Based on the ASHRAE Climate Zone definition, these five cities (Houston, LA, Baltimore, Columbus, and Minneapolis) are in climate zones 2A, 3B, 4A, 5A, and 6A, corresponding to the Hot-Humid, Warm-Dry, Mixed-Humid, Cool-Humid, and Cold-Humid zones, respectively. The simulation results provided valuable insights into determining the energy and cost saving potentials of precooling optimization for various real-world buildings. Beside research, we also created educationalvideos to promote green home technologies and published them on our extension website.

Publications

• Type: Journal Articles Status: Published Year Published: 2019 Citation: Jin, R. and Chen, Q. 2019. Overview of concrete recycling legislation and practice in the United States, Journal of Construction Engineering and Management, 145(4), https://doi.org/10.1061/(ASCE)CO.1943-7862.0001630.
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Jin, R., Yuan, H., and Chen, Q. 2019. Science mapping approach to assisting the review of construction and demolition waste management research published between 2009 and 2018, Resources, Conservation and Recycling, 140(January), 175-188, https://doi.org/10.1016/j.resconrec.2018.09.029.
• Type: Journal Articles Status: Published Year Published: 2019 Citation: Chen, Q., Romich, E., Cruse, A., Gorzitze, A., Shi, H., and Zhao, L. 2019. Surveying the edges: Homeowners perspectives on residential energy efficiency and renewable energy improvements in Ohio, Journal of Green Building, 14(1), 111-130.

Progress 10/01/17 to 09/30/18

Outputs
Target Audience: The target audiences include researchers in building energy and environment-related areas, professionals working in the architecture, engineering, construction, and facility management industry, building owners, and university students majoring in the related fields. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?PI Chen attended two international conferences to disseminate research findings to the building energy research community. A website has been developed to distribute the knowledge to building/home owners, building professionals, teachers, and students. What do you plan to do during the next reporting period to accomplish the goals?We plan to study the effects of other building parameters (e.g., occupancy schedule, internal loads, etc.) and external weather environments on building cooling energy cost reduction.

Impacts
What was accomplished under these goals? Thermal comfort is an important factor in designing high-quality buildings. The ill-conditioned environment always causes stress among building occupants and the negative impacts on the occupants' health and productivity. Heating, ventilation and air conditioning (HVAC) systems play important roles in providing and maintaining indoor thermal comfort for buildings. However, these systems are very energy-intensive. In the United States, HVAC systems consume around 40% of the total building energy. Therefore, maintaining an efficient HVAC system can not only offer a comfortable indoor environment for occupants but also save energy to protect our environment. Nevertheless, even under normal operations, various problems (e.g. biases of sensors, control command errors, obstructed air dampers, etc.) could occur in an HVAC system. Overall, these HVAC faults waste approximately 15-30% of energy and cause poor thermal comfort or even safety accidents. While the fault detection and diagnosis (FDD) for a building HVAC system becomes increasingly important, locating and isolating the faulty components is often a challenging task, especially when dealing with multistory buildings with complex HVAC systems. In recent years, using simulation software tools to study the building energy and thermal performance becomes popular. Numerous software (e.g. Energyplus, DOE-2, TRNSYS, etc.) are applicable to simulate the building energy and thermal performance accurately. Usually, the simulation model based on the manufacturer design data cannot capture the ill-conditioned performance caused by the faults of the building systems in real situation. Therefore, by comparing the thermal performance between the real situation and the simulation model, the faults of the building systems including HVAC can be detected. This researchadopted the model-based method to detect and diagnose the faults of a complex HVAC system in a multistory institutional building with the total gross floor area of 75,670 sq. ft. First, a simulation model of the case study building was created and validated based on both energy and thermal performance. Then, by comparing the indoor air temperatures between the simulation model and the real situation in 84 rooms, three types of the faults of the HVAC system were detected in summer. These include control faults, faults caused by defective parts, and HVAC system design faults. Similarly, the HVAC faults in winter were detected as well. For each type of faults, the corresponding solutions were proposed in this research, which could help building operators to quickly locate and fix the faults and improve their buildings' energy and thermal performance. The research findings will not only help lower buildings'energy and environmental impacts, but also improve the health and productivity of building occupants.

Publications

• Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Chen, Q. 2018. Challenges in developing teaching effectiveness and scholarship through service-learning projects, in Proc. ASC 54th Annual International Conference, Apr. 18-21, Minneapolis, MN.
• Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Shi, H., Liu, J., and Chen, Q. 2018. HVAC precooling optimization for green buildings: An RC-network approach, in Proc. 9th ACM International Conference on Future Energy Systems (ACM e-Energy), Jun. 12-15, Karlsruhe, Germany, 249-260.

Progress 10/01/16 to 09/30/17

Outputs
Target Audience:The target audiences include researchers in building energy and environment-related areas, professionals working in the architecture, engineering, construction, and facility management industry, building owners, and university students majoring in the related fields. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?PI Chen has given a talk at 2017 OSU Green Home Workshop that reached out to homeowners, students, and building professionals in the housing construction industry. What do you plan to do during the next reporting period to accomplish the goals?We plan to perform multi-cluster energy management by incorporating energy use reduction, renewable energy sources, and battery storage capacity at the building sites.

Impacts
What was accomplished under these goals? Precooling Strategies Studies: In the past year, we have performed the following three major research activities: 1) compared 10 HVAC scheduling strategies on minimizing peak load, total energy consumption, and total energy cost based on simulations, 2) developed analytical optimization formulations based on R-C thermal transfer models for optimal precooling scheduling, and 3) developed low-complexity algorithms to solve the formulated precooling problem with strong performance guarantee. Some of the main results include 1) choosing 25°C as night-setback temperature results in near optimal cooling energy consumption; 2) all the demand limiting (DL) strategies help reduce the peak load and the Load Weight-Averaging (LWA) method performs the best; and 3) the Extended Precooling (EPC) strategy combined with DL further reduces the peak load during the on-peak hours.

Publications

• Type: Journal Articles Status: Published Year Published: 2016 Citation: Abounia Omran, B., Chen, Q., and Jin, R. 2016. Comparison of data mining techniques for predicting compressive strength of environmentally friendly concrete, Journal of Computing in Civil Engineering, 30(6), http://ascelibrary.org/doi/abs/10.1061/(ASCE)CP.1943-5487.0000596.
• Type: Conference Papers and Presentations Status: Published Year Published: 2017 Citation: Chen, Q., Shi, H., and Belkofer, A. 2017. Challenges in the Building Information Modeling (BIM)/3D trade coordination process, in Proc. ASC 53rd Annual International Conference, Apr. 05-08, Seattle, WA, 503-510. http://ascpro.ascweb.org/chair/paper/CPRT159002017.pdf.

Progress 11/19/15 to 09/30/16

Outputs
Target Audience:The target audiences include researchers in building energy and environment related areas,professionals working in the architecture, engineering, construction, and facility management industry,building owners, and university students majoring in the related fields. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The Ph.D. studenton this project wastrained on a variety of cross-cutting disciplines. These include whole building energy simulation, data collection and processing, distributed algorithms. This multi-disciplinary training helps the studentbe better prepared for both industry and academia. How have the results been disseminated to communities of interest?The research results have been published in peer-reviewed journals. PI Chen has given a talk at OSU Symposium/Forum on Building Energy and Environments, which highlighted the key approaches and contributions of the project. What do you plan to do during the next reporting period to accomplish the goals?We plan to conduct research as outlined in the proposal. In particular, we will continue research efforts in optimizing the pre-heat and pre-cool control algorithms while maintaining comfortable indoor thermal environments.

Impacts
What was accomplished under these goals? Whole-Building Energy Simulation: To achieve the energy efficiency goal of cognitive green buildings, we began by developing holistic energy control schemes for a single building. A campus building, the 9-story New Dreese Laboratory (NDL), was selected as the case study for this research. This buildingalso provided a suitable testbed for developing an energy-efficient and occupant-satisfactory control algorithm. The building has more than 100 rooms, serving as classrooms, conference rooms, offices, laboratories, and computer center. In a previous study, more than 100 temperature sensor nodes were deployed in the NDL building, which formed a wireless sensor network, ThermoNet, to collect the temperature data during 2010-2011. In this research, a physical approach was adopted to build a predictive model for the NDL building by using the software tool of DesignBuilder. After the model was configured, simulation was performed for the tested periods (summer, winter, and the whole year). The accuracy of model prediction was evaluated by 1) comparing temperature distribution between simulation results and the historical data collected by ThermoNet, and 2) comparing the simulated energy performance between this study and another simulation study performed by a professional HVAC engineering firm. Energy and thermal comfort analysis: Once the model was calibrated and validated, we generated predictions for the building's indoor temperature during the summer and winter periods and annual energy consumption. Building energy consumption was analyzed based on end-use categories and benchmarking with the Commercial Buildings Energy Consumption Survey (CBECS), a national survey that records the energy consumption and expenditures data on commercial buildings. Through such analyses, energy usage abnormalities in this building were identified for the next step of this research. This study performed indoor thermal comfort analysis based on ANSI/ASHRAE Standard 55-2004. This standard describes two analysis methods: the graphical method for typical indoor environment and the computer model method for general indoor application based on the PMV-PPD index. The Predicted Mean Vote (PMV) is a heat balance equation, where six primary factors are proposed to be associated with thermal comfort: metabolic rate, clothing insulation, air temperature, radiant temperature, air speed, and humidity. The thermal scale used to quantify thermal sensation of occupancies ranges from hot (+3) to cold (-3). The Predicted Percentage of Dissatisfied (PPD) is related to the PMV index. The standard recommends that the acceptable PMV value should be from -0.5 to +0.5 and the PPD should be less than 10%. While the majority of rooms in this building had comfortable temperature based on the sensing data, some consistently hot and cold rooms were also identified. Based on the calibrated model, we will simulate building thermal conditions and energy use improvements based on the pre-cool and pre-heat control strategies to be proposed.

Publications

• Type: Journal Articles Status: Published Year Published: 2015 Citation: Q. Chen, L. Kleinman, and A. Dial, Energy Performance of Campus LEED Buildings: Implications for the Green Building and Energy Policy, Journal of Green Building, 10(3), 2015, pp. 144-167.
• Type: Journal Articles Status: Published Year Published: 2015 Citation: J. Liu, T. Kou, Q. Chen, and H. D. Sherali, On Wireless Network Infrastructure Optimization for Cyber-Physical Systems in Future Smart Buildings, International Journal of Sensor Networks, 18(3/4), 2015, pp. 148-160.