International High Performance Buildings Conference
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Recent documents in International High Performance Buildings Conference
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Thu, 09 Mar 2017 11:29:13 PST
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The Effect of the Configuration of the Absorber on the Performance of Flat Plate Thermal Collector
http://docs.lib.purdue.edu/ihpbc/245
http://docs.lib.purdue.edu/ihpbc/245
Mon, 12 Dec 2016 12:21:48 PST
In this study, a numerical thermal analysis for a new designed flat plate thermal collector was conducted through modeling. The new flat plate thermal collector has ellipse shaped tubes inside a wavy shaped absorber, which is made of stainless steel. For the comparison, the conventional flat plate thermal collector with circular copper tubes served as a base case was also modeled. HottelWhillier equations were utilized to formulate thermal networks for both models developed in Engineering Equation Solver. According to simulation results, for a given solar radiance, the thermal efficiency of both solar collectors decreases along the increase of the operating temperature of the fluid. Given a same operating temperature of fluid, higher solar radiances lead to higher efficiency of both thermal collectors. Comparably, the new flat plate thermal collector generally obtains a 10% higher efficiency than the conventional one at different solar radiance level and average operation temperature of towing to its new design of the absorber. This concludes that the new flat plate solar collectors would be a better option for typical solar domestic hot water system.
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Moyu Yan et al.

Recovery of Waste Thermal Energy in U.S. Residential Appliances
http://docs.lib.purdue.edu/ihpbc/244
http://docs.lib.purdue.edu/ihpbc/244
Mon, 12 Dec 2016 12:21:45 PST
With the United States being the worldâ€™s second largest consumer of primary energy, research into areas of significant consumption can provide large impacts in terms of the global energy consumption. Buildings account for 41% of US total energy consumption with the residential sector making up a majority. Household appliances account for the second largest site energy consumption at 27%, after the HVAC system for the U.S. residential sector. By quantifying the expected energy available in the waste stream for five major appliances; household refrigerator, clothes dryer and washer, dishwasher, and cooking oven, a potential energy source is presented. A cold water cooling stream is applied to the waste stream of each appliance and an estimated amount of energy can be recovered. The household refrigerator is modeled having an increase in cooling capacity of about 12% and a reduction on compressor power consumption of about 26%. A sample operation of the clothes dryer has the exhaust air stream being cooled down to 30.5Â°C (86.9Â°F) or on the other side, is able to heat 19 liter (5 gal) of water up to about 54.5Â°C (130.1Â°F). Large volumes of water are available by the clothes washer, but due to typical operation characteristics, low wash and rinse temperatures, the waste stream was not high in temperature. While the dishwasher provided higher heat source temperatures, 40Â°C (104Â°F), than the clothes washer, 36Â°C (97Â°F), the opposite was true. The volume of waste water drained is very low compared to the clothes washer 11.7 liter (3.1 gal) to 155 liter (41 gal). Thus water temperatures in the storage tank did not reach above 30Â°C (86Â°F) even with low storage volumes. The cooking oven can generate very high water temperatures depending how small of a storage tank is connected. Further work in this area is recommended due to the potential of high water temperatures generated from residential waste energy streams not currently being captured, and thus can offset some siteenergy usage.
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Stephen L. Caskey et al.

Evaluation of Optimal Chiller Plant Control Algorithms in ModelBased Design Platform with HardwareintheLoop
http://docs.lib.purdue.edu/ihpbc/243
http://docs.lib.purdue.edu/ihpbc/243
Mon, 12 Dec 2016 12:21:42 PST
Chiller systems account for 31% of the total cooling electricity consumption of mediumsized commercial buildings within 25k200k square feet. In the last decade, advanced controls such as model predictive control (MPC) has demonstrated energy savings that typically range from 5% to 15%. However, the installation and commissioning efforts to deploy MPC into existing building automation system (BAS) are often cost prohibitive and therefore undermine the energy saving benefit it brings into the game. This paper presents a framework and results of using modelbased design (MBD) to evaluate the benefit and tradeoffs of different chiller plant control algorithms for mediumsized commercial buildings including an optimizationbased algorithm that can be deployed rapidly with little installation and commission effort. A highfidelity dynamic simulation model for selected building types and climate zones were developed and implemented in the hardwareintheloop (HiL) platform. Baseline and optimizationbased control algorithms were deployed in Automated Logic Corporation (ALC) controller hardware with their performance monitored through WebCtrl in realtime. The first contribution of this paper is the development and successful integration of Modelicabased highfidelity dynamic models of chiller plants, airhandling units, and building envelope and zones. The building types of medium office and large hotel were selected and modeled in details. In particular, the building envelope and zone models were developed based on a direct translation of the selected DOE EnergyPlus reference building models, which are widely accepted in the building modeling community. The chiller plant was modeled with physicsbased components such as chillers, pumps, valves, and pipes that include typical dynamics in a real chiller plant. Both primarysecondary and primaryonly configurations were modeled and considered in the controls evaluation. The air handling unit was modeled based on the component models from Modelica Buildings Library developed by LBNL and includes a finitevolume based cooling coil model capable of calculating latent heat transfer. The second contribution of this paper is the demonstration of utilizing HiL platform to benchmark baseline and optimal control algorithms based on detailed wholebuilding level dynamic models. In the HiL setup, a realworld hardware controller is coupled to the highfidelity simulation model and operates in realtime. The HiL setup provides the same interface for installation of overlay software as it would be a demonstration site BAS, eliminates the risk associated with seasonal operation and availability in demonstration sites, enables precise evaluation of energy savings potential for various internal and external building load scenarios.
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Pengfei Li et al.

On The Representation Of The Thermal And Visual Behavior Of A Roller Shade Material: Comparison Between Different Simulation Models
http://docs.lib.purdue.edu/ihpbc/242
http://docs.lib.purdue.edu/ihpbc/242
Mon, 12 Dec 2016 12:21:39 PST
Shading systems, if efficiently operated, can improve the internal environmental quality, namely both thermal and visual comfort, and reduce the energy consumption due to cooling needs. Roller shades represent one of the most commonly used types of shading systems, in particular in the tertiary sector. Not only they can be easily installed and maintained, but also they often represent the only design choice when existing buildings are considered. Although roller shades are characterized by a beambeam and by a beamdiffuse transmittance, both changing according to the incidence angle, as the transmitted solar radiation decreases with the increase of it, they are typically modelled assuming equal reflectance and emissivity for both sides and perfect diffuser behavior, with transmittance and reflectance independent from the solar radiation incidence angle. Neglecting the daily variability of these properties can lead to underestimate their impact on the occupants comfort conditions. In this paper, different models for representing the roller shades behavior, embedded in two widely diffuse simulation codes have been compared with a set of measured data, recorded at the Bowen laboratories of the Purdue University (Indiana USA), combining thermal (Energy Plus) and lighting simulation (Energy Plus or DIVA for Rhino). The thermal properties of the building materials and the internal gains have been calibrated for the thermal simulation, in order to evaluate better the models capability of predicting the roller shades behavior. Then, starting from the simplest daylighting model, which assumes the roller shades as perfect diffusers, more complex characterizations have been considered and validated through the comparison with the measured data.Â
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Anna Maria Atzeri et al.

Adaptive Personalized Shading Control Strategies To Maximize Occupant Satisfaction While Reducing Lighting Energy Use In Buildings
http://docs.lib.purdue.edu/ihpbc/241
http://docs.lib.purdue.edu/ihpbc/241
Mon, 12 Dec 2016 12:21:35 PST
In this study, a personalized shading control framework is developed to maximize occupant satisfaction while minimizing lighting energy use using a multiobjective optimization scheme. A personalized satisfaction model was developed based on speciallydesigned experiments in private offices, to quantify the occupant satisfaction level with motorized roller shades by predicting the override probability of occupants considering different variables. Then, a multiobjective optimization algorithm was constructed, considering the shading override probability and predicted lighting energy use as objectives, where the occupants are the decision makers in the final balancing between their personalized comfort limits and energy use considerations. The developed method serves as a prototype study on adaptive shading controls with learned personalized comfort profiles and parallel energy use considerations.
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Jie Xiong et al.

Reducing energy consumption for buildings under system uncertainty through robust MPC with adaptive bound estimator
http://docs.lib.purdue.edu/ihpbc/240
http://docs.lib.purdue.edu/ihpbc/240
Mon, 12 Dec 2016 12:21:33 PST
Model Predictive Control (MPC) has emerged as an alternative to traditional control method to reduce building energy consumption. With the presence of model uncertainty, such as mismatch between the plant and controloriented model, the use of MPC may result in thermal comfort violation or energy waste. The influence of model uncertainty becomes even more significant as the size and complexity of the investigated building increase. Robust MPC (RMPC), which requires knowledge on the system uncertainty, has been investigated for enhancing the stability of MPC. However, the implementation possibility of the RMPC is prevented by increased computational burden and conservativeness of controller performance. This paper deals with the latter issue by presenting a novel adaptive RMPC scheme for temperature regulation in commercial buildings. The novelty comes from the development of a comparison model built based on a nonlinear autoregressive model for worstcase analysis. This comparison model enabl es us to transform a linear, robust MPC problem into an adaptive one with a timevarying uncertainty bound. The proposed method is tested on a simulation model developed from building data collected from a spacious hall at an airport terminal. By conducting simulation using different MPCs, it is found that the proposed RMPC method is able to behave robustly against uncertainty with the least performance loss. This means the maximum energy saving and the least thermal comfort violation.
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Hao Huang et al.

Evaluation of an Extremum Seeking Control Based Optimization and Sequencing Strategy for a Chilledwater Plant
http://docs.lib.purdue.edu/ihpbc/239
http://docs.lib.purdue.edu/ihpbc/239
Mon, 12 Dec 2016 12:21:30 PST
Chilledwater plants with multiple chillers account for a significant fraction of energy use in large commercial buildings. Realtime optimization and sequencing of such plants is thus critical for building energy efficiency. Due to the cost and complexity associated with calibrating a chiller plant model to field operation, modelfree control has become an attractive solution. Recently, Mu et al. (2015) proposed a modelfree realtime optimization and sequencing strategy based on extremum seeking control (ESC) for chilledwater plants with multiple parallel chillers. In this ESC scheme, the variable to be optimized is the total power from the chiller compressors, cooling tower fans, condenser and evaporator loop water pumps, while the manipulated inputs include the tower fan airflow, condenser water flows and evaporator leaving chilledwater temperature setpoint. Two schemes are proposed for chiller sequencing: A) A chiller is turned on based on the measurement of chilled water valve position and is turned off when a chiller compressor is running at its nominal minimum speed. B) A chiller is turned on and off based on the measurement of operating cooling load. For Scheme A, Mu (2015) performed a comprehensive case study, and the simulation results demonstrated that the proposed framework performed well under various ambient, load and equipment conditions. However, for Scheme B, only one case was simulated. Although the two chiller sequencing schemes have a shared physical process in terms of chiller plant operation, it is necessary to evaluate the loadbased Scheme B in terms of energy efficiency performance. This paper aims to provide a comprehensive evaluation for the Scheme Bbased optimization and sequencing strategy for a multichiller chilledwater plant. Three ambient conditions are considered: i) 27 °C and 60%RH (Mild), ii) 37°C and 30%RH (Dry Hot), and iii) 37 °C and 80%RH (Humid Hot). For each of these ambient conditions, simulations are performed for the scenarios listed below, Scenario #3 is simulated with dynamic ambient and load profiles. Scenario #1. Twochiller ESC with no sequencing under fixed ambient conditions Scenario #2. Chiller sequencing under variable load and fixed ambient conditions Scenario #3. Chiller sequencing with realistic ambient and load profile Scenario #4. Penalty Function based ESC Chiller Sequencing Scenario #5. ESC for Efficiency Recovery: Chiller A properly charged and Chiller B with a low refrigerant charge Scenario #6. ESC for Efficiency Recovery: Chiller A with nominal operation and Chiller B with heat exchanger fouling References B. Mu, Y. Li, T.I. Salsbury, J.M. House, Extremum Seeking Based Control Strategy for a ChilledWater Plant with Parallel Chillers," ASME Dynamic Systems and Control Conference, Columbus, OH, paper no. 9949, 10 pages, 2015 B. Mu, Selfoptimizing Control for Building Ventilation and Air Conditioning Systems,Ph.D.Dissertation, Department of Electrical Engineering, University of Texas at Dallas, December 2015.
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Zhongfan Zhao et al.

StateSpace Modeling of Thermal Spaces in a MultiZone Building
http://docs.lib.purdue.edu/ihpbc/238
http://docs.lib.purdue.edu/ihpbc/238
Mon, 12 Dec 2016 12:21:27 PST
A study on system identification and modeling of thermal spaces in a large institutional building is presented. The main topic of this paper is how the optimum model order associated which each thermal zone depends on factors such as the location of the zone within the building, its orientation and its exposure to outdoor space. Thermal models are essential in predictive control since they are required to predict the thermal load of a single building zone, a collection of various thermal spaces, or a whole building. The results of this study will serve as a guideline for choosing the appropriate order of linear models in similar buildings. The case study building is a model of a two storey school with a floor area of 24,000 m2 (258,000 ft2). The detailed thermal model of the building is created in EnergyPlus. This building model consists of 46 thermal zones covering a large variety of spaces: small offices, classrooms, long hallways and two gymnasia. The EnergyPlus is used to generate yearly input and output data available at 10minute intervals; this data is used in a methodical system identification exercise, resulting in a set of multiinput singleoutput (MISO) statespace linear models. The challenge in modeling the thermal zones is to develop a relatively loworder model such that the thermal response of each zone is calculated by incorporating the effect of diverse inputs, such as outdoor factors (solar gains and outdoor temperature) as well as indoor factors, e.g., internal gains and heating and cooling energy delivered to the zone. Moreover, in a multizone building, accounting for the thermal effect of adjacent zones on one another is also an important factor to be taken into account. It has been found that this additional complexity requires careful selection of the inputs to the linear models, e.g., it might be helpful to include the heating/cooling delivered to adjacent zones.
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Vahid Raissi Dehkordi et al.

Airflow Based Model to Estimate Commercial Building HVAC Energy Use: Analysis to determine principal factors for different climate zones
http://docs.lib.purdue.edu/ihpbc/237
http://docs.lib.purdue.edu/ihpbc/237
Mon, 12 Dec 2016 12:21:24 PST
The paper presents an airflow based modeling method to estimate HVAC energy consumption in large commercial office buildings. The model was developed by analyzing operational data from building automation system, relating load profiles and efficiency of key HVAC equipment, Â based on economizer control policies that determine outside and return airflow rates.Â The model predicts annual energy use for buildings HVAC loads based on Â hourly climate data (temperature and relative humidity), and building airflow requirements. Â The model determines HVAC energy use in terms of building airflow rates for systems utilizing central air handler units with economizers, and identifies the key consumption drivers. Â Some parts of the model (economizer performance and fan energy use) were based on data collected from the building automation system, relating major component energy use in terms of airflow rates and control laws.Â Data was obtained for three commercial scale officelab buildings at Boston University. Â Results are given in terms of major contributors to HVAC energy use and cost in terms of heating, cooling, and fan motor power for 4 different US cities. Â A comparison to other building HVAC models used to disaggregate CBECs data is presented.
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Stefan Gunnsteinsson et al.

Temperature, Relative Humidity, and CarbonDioxide Modulation in a NearZero Energy Efficient Retrofit House
http://docs.lib.purdue.edu/ihpbc/236
http://docs.lib.purdue.edu/ihpbc/236
Mon, 12 Dec 2016 12:21:21 PST
The concept of Net Zero Energy Buildings (NZEB) has reached a phase where countries all around the world are encouraging its implementation into mainstream construction. In the United States, both private and public sector buildings are incorporating energy efficient technologies to reduce their environmental impact, while increasing the productivity and comfort of its occupants. A Net Zero Energy Building (NZEB) performs as expected only when the buildingâ€™s envelope, HVAC and other mechanical/electrical systems work in unison. Subsequently, once these buildings are occupied, the behavior of its occupants significantly influences the buildings energy performance.Â The authors have captured the modulation of temperature, relative humidity, and carbondioxide within one such NearNet Zero Energy Building during the heating season. This house is a DeepEnergy Retrofit Home completed as a marketing and demonstration home for a joint neighborhood stabilization project and U.S. Department of Energy funded communitywide retrofit grant program in Lafayette, Indiana. The house includes an internet based realtime home energy monitoring system, which facilitates reviewing the changes in the houses energy performance as a consequence of fluctuating internal temperature settings and external climate conditions. Post retrofit blowerdoor test result conform that the house has been made fairly airtight during the retrofit. Hence ventilation within the house is achieved via an Energy Recovery Ventilator (ERV) with multiple stages of operation. To this end, the paper is an exploratory examination of the interrelationships between occupancy, interior temperature, relative humidity, carbondioxide levels and energy consumption within the retrofitted residential NearNet Zero Energy Building.
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Saurabh Sudhakaran et al.

Automated Fault Diagnostics for AHUVAV Systems: A Bayesian Network Approach
http://docs.lib.purdue.edu/ihpbc/235
http://docs.lib.purdue.edu/ihpbc/235
Mon, 12 Dec 2016 12:21:18 PST
Although it is widely accepted that 20%30% of total HVAC energy in commercial buildings is wasted due to faulty or inefficient operation, there presently exist no widely adopted solutions to identify and remedy this waste. This lack of adoption is due primarily to the high upfront costs associated with the manual process of customizing commissioning solutions for each individual building. In order to reduce these costs, diagnostic technologies must automate the process of installing and customizing solutions for each implementation. A novel approach to addressing this problem for air handling units (AHUs) and variable air volume (VAV) boxes is presented here. This strategy utilizes a Bayesian network to identify and understand the system operation, identify faults that are wasting energy or impacting occupant comfort, and then generate performance baselines against which future operation will be compared. When this algorithm is first connected to a new building, an adaptive diagnostic Bayesian network identifies the components and configuration of the AHUs and VAVs, and then generates probabilistic outputs indicating the root causes of potential faults and inefficiencies. Once these issues have been rectified to the satisfaction of the building operator, the algorithm then begins to accumulate training data with detailed information about the system operation (while simultaneously continuing to monitor for additional faults). As this training data is accumulated, the diagnostic confidence of the Bayesian network is continuously improved. Additionally, this use of an operational baseline allows for accurate detection and diagnosis of faults causing gradual performance degradation in addition to faults that abruptly occur.
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Adam Regnier et al.

A Study of the Effect of Zone Design Parameters on Frequency Domain Transfer Functions for Radiant and Convective Systems
http://docs.lib.purdue.edu/ihpbc/234
http://docs.lib.purdue.edu/ihpbc/234
Mon, 12 Dec 2016 12:21:16 PST
This paper presents a parametric study on the effect of a number of room design parameters for radiant and convective heating sources as well as solar gains. This study is performed using frequency domain modeling approach by means of which important room transfer functions are obtained and studied. Frequency domain modeling is a useful tool for analyzing building thermal dynamics as well as different design options. The phenomena affecting energy consumption inside a building such as solar gains, exterior temperature and heating/cooling sources are usually cyclic phenomena and can be modeled by means of frequency domain techniques assuming periodic conditions in the calculations. Using frequency domain techniques, the transient heat conduction inside the walls can be accurately modeled with no discretization for the thermal mass. However, there is difficulty modeling timevarying variables in the frequency domain. This is especially important in the case of convective and radiative heat transfer coefficients which are inherently nonlinear elements. The coefficients are usually linearized in order to have a linear system of equation that can be presented by means of a linear thermal network[1]. In frequency domain modeling approach usually a constant value for the convective and radiative heat transfer coefficients is assumed. However, this assumption can produce significant errors when there are large differences between surfaces temperatures for example in the case of floor heating or direct gain rooms with large windows[2]. In this case, a sensitivity analysis on the magnitude of the important room transfer functions considering different values for convective and radiative heat transfer coefficients needs to be done. A room is considered with different types of heating (convective and radiative heating sources) and different levels of thermal mass on the floor. The effect of thermal mass and floor covering on the room thermal response considering different types of heating is investigated. Magnitude of the transfer functions between room air temperature and the convective heating source is a determining element in the room air temperature fluctuations considering thermal comfort aspects. Also, in the case of radiant heating, the transfer function between room air temperature and radiant heat source can be used to determine the room air temperature swings due to the floor radiant heating source. The sensitivity of the magnitude of the transfer functions versus different values of convective and radiative heat transfer coefficients is studied and compared. This study will guide future model predictive control (MPC) research by means of frequency domain techniques to make choices such as optimal thermal mass thickness for floor heating versus convective systems. It will contribute to linking design with MPC. [1] Athienitis, A.K. and O'Brien, W., Eds. (2015). Modelling, design and optimization of netzero energy buildings, Solar heating and cooling, Berlin: Ernst, Wilhelm & Sohn 2015. [2] Saberi Derakhtenjani, Ali, Candanedo, Jos A., Chen, Yuxiang, Dehkordi, Vahid R., Athienitis, Andreas K. (2015), Modeling approaches for the characterization of building thermal dynamics and modelbased control: a case study. ASHRAE STBE (Science and Technology for the Built Environment) Journal (21): 824836.
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Ali Saberi Derakhtenjani et al.

A Bayesian Approach for Learning and Predicting Personal Thermal Preference
http://docs.lib.purdue.edu/ihpbc/233
http://docs.lib.purdue.edu/ihpbc/233
Mon, 12 Dec 2016 12:21:12 PST
Typical thermal control systems automated based on the use of widely acceptable thermal comfort metrics cannot achieve high levels of occupant satisfaction and productivity since individual occupants prefer different thermal conditions. The objective of this study is to develop environmental control systems that provide personalized indoor environments by learning their occupants and being selftuned. Towards this goal, this paper presents a new methodology, based on Bayesian formalism, to learn and predict individual occupants thermal preference without developing different models for each occupant. We develop a generalized thermal preference model in which our key assumption, Different people prefer different thermal conditions is explicitly encoded. The concept of clustering people based on a hidden variable which represents each individuals thermal preference characteristic is introduced. Also, we exploited equations in the Predicted Mean Vote (PMV) model as physical knowledge in order to facilitate modeling combined effects of various factors on thermal preference. Parameters in the equations are reestimated based on the field data. The results show evidence of the existence of multiclusters in people with respect to thermal preference.
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Seungjae Lee et al.

A Distributed Model Predictive Control Approach for Optimal Coordination of Multiple Thermal Zones in a Large Open Space
http://docs.lib.purdue.edu/ihpbc/232
http://docs.lib.purdue.edu/ihpbc/232
Mon, 12 Dec 2016 12:21:09 PST
Model Predictive Control (MPC) based approaches have recently seen a significant increase in applications to the supervisory control of building heating, ventilation and airconditioning (HVAC) systems, thanks to their ability to incorporate weather, occupancy, and utility price information in the optimization of heating/cooling strategy while satisfying the physical constraints of HVAC equipment. Many of the proposed MPC solution approaches are centralized ones that often have difficulty in dealing with largescale building clusters or even a single building consisting of multiple thermal zones due to the high computational cost caused by the large number of decision variables and the overhead in information gathering and distribution. This paper investigates a decomposition technique based on a variant of the Alternating Direction Method of Multipliers (ADMM) that can significantly reduce the computational and communication costs of centralized solutions, and more importantly, facilitates a plugandplay implementation. The proposed Distributed Model Predictive Control (DMPC) framework takes advantage of parallel computation and collaborations among multiple agents, each of which is assigned to address a smaller dimensional optimization problem. The proposed method is general in that it can accommodate some fairly general types of couplings in agent dynamics as well as in the cost function; therefore it can be used for a broad class of HVAC optimal operation problems. A case study on one of the Purdue Living Labs is carried out to demonstrate the effectiveness of the proposed method. The Living Lab considered is an open office space served by one central air handling unit (AHU) with multiple diffusers whose openings can be controlled individually. The office space is partitioned into multiple thermal zones with individual thermostat controls. In view of significant thermal couplings due to direct air exchange and noticeable load gradient between zones, a multiple thermal zone coordination problem is formulated with the objective of optimally scheduling the different thermostat setpoints for energy minimization and comfort delivery while satisfying actuation constraints. Preliminary results show that the proposed method successfully converges to the optimal solution for the problem concerned in this case study. The optimal solutions demonstrate significant opportunities of interzonal coordination and energy savings potentials.
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Xiaodong Hou et al.

Model Selection for Predicting the Return Time from Night Setback
http://docs.lib.purdue.edu/ihpbc/231
http://docs.lib.purdue.edu/ihpbc/231
Mon, 12 Dec 2016 12:21:06 PST
Night setback is a common strategy used to reduce energy use in buildings. It involves increasing the cooling setpoint and decreasing the heating setpoint in a zone during unoccupied periods. To ensure occupant comfort and maximize energy savings, the zone temperature must be returned to the range defined by the occupied cooling and heating setpoints at occupancy, but not before. The time required to cool down or warm up a zone from a night setback condition is referred to as the return time and algorithms for predicting return time are commonly referred to as optimal start algorithms. Optimal start algorithms generally employ a model for predicting return time. This study describes the selection of separate return time models for cooling (i.e., a model for predicting the return time when cooling is required) and heating from 57 candidates. The following model forms were considered: Ï„ = f (Tf  Ti), Ï„ = f ((Tf  Ti), u), Ï„ = f ((Tf  Ti), Tout), and Ï„ = f ((Tf  Ti), u,Â Tout) where Ï„ is the return time, Tf is the zone temperature at the end of the optimal start period, Ti is the zone temperature at the beginning of the optimal start period, u is exponentially weighted moving average (EWMA) of the zone cooling or heating demand at the beginning of the optimal start period, and Tout is the outdoor air temperature at the beginning of the optimal start period. Computer simulations were used to generate yearlong data sets relating return time to the model inputs. The simulations considered the influence of climate, building mass, controller tuning, zone orientation, and the unoccupied control strategy on the return time. In all, 140 cooling data sets and 104 heating data sets were generated. For each data set, least squares regression was performed to determine the parameters for each of the 57 models considered. The performance of each model was quantified using the average root mean square prediction error across all simulations. The study revealed that the best models for predicting return time use the zone temperature change and the EWMA of the zone cooling or heating demand as inputs. The EWMA of the zone cooling or heating demand provides an indication of the recent history of the cooling or heating load on a zone and can account for intermittent cooling or heating that is required to keep the zone temperature within the bounds of the unoccupied setpoints. Notably, outdoor air temperature, a common input in optimal start algorithms, is not used.Â To the best of the authors' knowledge,Â zone cooling and heating demand have not been previously used as an input in an optimal start algorithm. The full paper will provide a detailed description of the simulations and model comparison undertaken in this study.
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John E. Seem et al.

A Multilevel MPC Simulation Study in a School Building
http://docs.lib.purdue.edu/ihpbc/230
http://docs.lib.purdue.edu/ihpbc/230
Mon, 12 Dec 2016 12:21:04 PST
This paper presents results obtained by applying a multilevel methodology for the implementation of a modelpredictive control (MPC) strategy in a large institutional building. The case study building, is a model of a two storey school building, with a floor area of 24,000 m2 (258,000 ft2) with 46 thermal zones. The zones considered include a large diversity of spaces: small offices, classrooms, long hallways and two gymnasia. A detailed thermal model of the building was created in EnergyPlus. The EnergyPlus was used to generate input and output data employed for a systematic system identification exercise, which resulted in a set of multiinput singleoutput (MISO) linear models. Three control levels were considered: a thermal zone level (46 models), â€œwingâ€ level (7 models) and a building level (one model). The models identified are statespace representations with order ranging between 4 and 12. This hierarchical, multilevel methodology enables the use of loworder models for each system under consideration: for example, a simple 9th order model at the building level can be used to predict its thermal load over a 48h horizon, with a relatively coarse sampling time of 2 hours (24 samples). At the other extreme, a zone level model has a prediction horizon of 2 hours, and a much finer sampling time of 10 minutes (12 samples). For the MPC studies, a mechanical system considering thermal energy storage devices (ice bank + hot water tank) was considered in the calculations. An optimization routine was carried out to minimize the electricity cost, while maintaining comfortable conditions in the space: a timeofuse rate was employed in the definition of the objective function. The results presented in this paper illustrate how the multilevel concept discussed in this paper can be used to harmonize the performance of building control systems, from the supervisory BEMS to the local thermostat controllers.
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Vahid Raissi Dehkordi et al.

Controloriented Modelling of Thermal Zones in a House: a Multilevel Approach
http://docs.lib.purdue.edu/ihpbc/229
http://docs.lib.purdue.edu/ihpbc/229
Mon, 12 Dec 2016 12:21:00 PST
This paper presents a multilevel approach to the problem of modelling different thermal zones in a house for control applications. This problem has been treated before by modelling the whole house with a single, allinclusive RC circuit which may have different levels of resolution. The core of the proposed methodology lies in the possibility of allowing the user to switch back and forth between models representing different control levels according to the modelling objectives. For the development of specific control algorithms for each zone, the house can be treated as a collection of interconnected zonal models, as opposed to a single, large model. This modelling approach has the advantage of maintaining a simple structure for each zone, while also taking into account the heat transfer between zones; at this control level, issues such as occupancy, thermal comfort or setpoint profiles can be examined in detail. On the other hand, if the user is interested in a quick estimate of global variables (e.g., overall thermal load over the next 24 h) then different zones or even the entire house may be combined into a single loworder model. In summary, this multilevel approach allows the user to â€œzoom in and outâ€ so that models at each control level remain manageable, easy to calibrate and easy to physically interpret. This paper uses data from an existing unoccupied test house, representative of a typical family home in QuÃ©bec, as a case study. Four zones are considered: basement, main floor, upper floor and the attached garage. For the most detailed analysis, these zones are modelled with four interconnected zone models. Alternative ways of combining zones are investigated. A global loworder house model is used to calculate the thermal load of the building. Results of thermal load calculations are compared and discussed.
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Jennifer Date et al.

Investigation on A Ground Source Heat Pump System Integrated With Renewable Sources
http://docs.lib.purdue.edu/ihpbc/228
http://docs.lib.purdue.edu/ihpbc/228
Mon, 12 Dec 2016 12:20:58 PST
Buildings consumed 40% of the energy and represented 40% of the carbon emissions in the United States. This is more than any other sector of the U.S. economy, including transportation and industry. About 24% of all energy used in the nation is for space heating, cooling and water heating in buildings. Enhancing building efficiency represents one of the easiest, most immediate and most cost effective ways to reduce carbon emissions. One of energy efficient and environment friendly technologies with potentials for savings is Ground Source Heat Pump (GSHP) system. On the other hand, solar energy is considered as an unlimited and an environment friendly energy source, which has been widely used for solar thermal and solar power applications.Â This paper presents a laboratory test facility for a solar powered ground source heat pump system. The ultimate technical goal is to apply the solar powered ground source heat pump into a netzero energy building, where all the electricity consumption will be covered by an integrated onsite solar PhotovoltaicsÂ (PV)Â panels and battery system. The addedon benefits from this solar powered GSHP include but not limited to: 1) help further reduce electricity peak demand and 2) help further reduce greenhouse emissions. In this test rig, a Â¾  ton watertoair GSHP is connected to two 60feet deep wells.Â A group of solar PV panels of 1.12KW is connected to a battery bank, which is used to power the GSHP and a 0.27KW DC powered well pump. During the daytime, solar PV panels convert solar photons into electrical energy which will be stored into the battery bank. Whenever the GSHP system is on demand, the battery bank will provide the power. This test rig also has a comprehensive performance monitoring and data acquisition system. Well groundwater temperatures, refrigerant temperatures, air temperatures, water flow rates, etc. are all realtime monitored, trended and stored.Â In addition, an onsite weather station is installed to measure outside air temperature, relative humidity, wind speed and direction, and solar radiation. The details for the design and layout of this solar powered GSHP, together with the monitoring and data acquisition system will be introduced in this paper. In addition, the preliminary data collected from a testing of a cooling mode operation will be presented to illustrate the benefits of the proposed system. Finally, the feasibility of the application of the system will be discussed in the paper.
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Defeng Qian et al.

A Multiobjective Approach to Optimal Battery Storage in The Presence of Demand Charges
http://docs.lib.purdue.edu/ihpbc/227
http://docs.lib.purdue.edu/ihpbc/227
Mon, 12 Dec 2016 12:20:55 PST
In this paper, we propose an optimization framework for optimal energy storage, in the form of batteries, by residential customers. Our goal is to determine the value of battery storage to those customers whose electricity bills consist of both TimeofUse charges ($/kWh, with different rates for onpeak and offpeak hours) and demand charges ($/kW, proportional to the peak rate of consumption in a month). The customers may have access to a local power generating source in the form of solar PhotoVoltaic (PV). In order to quantify the benefits from the battery storage, we pose a battery optimization problem which minimizes the monthly electricity bill 30 poffÂ âˆ‘kâˆˆoff q(k)Î”t +Â 30 pon âˆ‘kâˆˆon q(k)Î”t + pd supkâˆˆon q(k), where poff, pon,Â pd are the offpeak, onpeak and demand prices, and q(k) is the power delivered by the utility company to the customer. We consider this power to be used according to q(k) = qb(k) + qa(k)  qsolar(k), where qa is the power consumed by the appliances, qsolar is the power provided by the solar PV, andÂ qb is the power given to or taken from the battery. We assume that the rate of the energy stored in the battery is proportional toÂ qb and the stored energy is bounded by the batteryâ€™s capacity (kWh). Furthermore, we account for the battery degradation by modeling the batteryâ€™s capacity as a function of the number of charging/discharging cycles and the depth of discharge. Because of the presence of demand charges (supk q(k)), the objective function of our battery optimization problem is not separable in time  a property (time separability) which is a sufficient for the dynamic programming algorithm to converge to an optimal solution. We establish a provably convergent algorithm for the nonseparable optimization problem in the following two steps. First, we replaceÂ supk q(k) in the objective function using the following approximation Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â supkâˆˆon q(k) = q(k) lâˆž = (âˆ‘kâˆˆon q(k)p)1/p for some large p. Then, we construct a multiobjective problem (a class of optimization problems involving at least two objective functions to be minimized simultaneously) defined by a parameterized set of dynamic programs expressed in terms of the timeseparable functions J1(q) = âˆ‘k q(k) J2(q) = âˆ‘kâˆˆon q(k)p Each of these parameterized dynamic programs can be solved using the standard dynamic programming algorithms. The set of solutions to these parameterized problems form a Pareto front  a set which is guaranteed to contain the solution to the original battery optimization problem as p â†’ âˆž. We apply our algorithm to multiple scenarios described by a range battery sizes, solar generation levels and appliances loads to quantify the savings from the batteries for a wide range of residential customers. The proposed approach can be potentially used to: 1) Model customers response to changes in electricity prices; 2) Quantify the benefits of energy storage to utility companies.
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Reza Kamyar et al.

Load and Electricity Rates Prediction for Building Wide Optimization Applications
http://docs.lib.purdue.edu/ihpbc/226
http://docs.lib.purdue.edu/ihpbc/226
Mon, 12 Dec 2016 12:20:51 PST
The reduction of energy consumption, use of renewable energy, and preservation of natural resources are becoming increasingly important. Several applications in the energy efficiency field aim at minimizing energy consumption and/or cost. To achieve this, these applications employ optimization techniques that require future prediction of the performance and various loads of a facility, campus, building, or an energy plant, such as hot water, cold water, and/or electric load. The prediction horizon may be as short as few hours to ten days into the future, depending on the application at hand. Furthermore, for the purpose of minimizing electricity cost, it is as necessary to know, as accurately as possible, what the electricity rates are over a given horizon. Therefore, a method for predicting hot water, cold water, and electric loads and electricity rates over a given horizon into the future has been developed (Load will be used to refer to hot water, cold water, and electric loads and electricity rates without loss of generality). The method developed takes into consideration the several factors contributing to the load value. These factors include time of day, day of week, schedules (insession or outofsession for a university campus for example), and weather (temperature and humidity). The load predicted consists of a deterministic term and a stochastic term. The deterministic term is calculated using linear regression models, whose coefficients are determined offline. These models rely on the typical load value for a given time of day and daytype (days with similar load profiles) and weather forecast. The latter is obtained from the National Oceanic and Atmospheric Administration (NOAA) through their National Digital Forecast Database (NDFD) service. The stochastic term is determined using an AutoRegressive (AR) model, whose coefficients are determined offline. The AR model calculates future prediction errors based on the current prediction error. The stochastic element of the predicted load gives the method developed its adaptive property, and thus increases the accuracy of the prediction by updating the forecast using current measurements of the load. Historical weather and load data are used for determining the coefficients of the regression models and the AR model offline. For a given set of training data, the method developed generates a set of regression models for each daytype. Daytypes are determined by a daytyping algorithm which specifies days with similar load profiles based on cluster analysis techniques. Outside air enthalpy and a typical load profile constitute the predictors variables in each set of regression models. Each daytype is characterized by a different typical load profile which is generated using an optimal data fitting technique. The AR model coefficients are determined using the residuals obtained from different sets of regression models. Given the determined models, the current load measurement, and weather forecast, the future load values are calculated by selecting the appropriate regression model and summing the deterministic and stochastic terms.Â
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Mohammad N ElBsat et al.