Numerical Study of Convective Heat Transfer for Flat Unglazed Transpired Solar Collectors

Convective heat transfer coefficients (CHTC) for flat unglazed transpired solar collectors have been computed using high-resolution 3-dimensional steady RANS CFD simulations. The Standard k-ε, Renormalization Normal Group k- ε (RNG k-ε), Realizable k-ε and Shear Stress Transport k-ω (SST k-ω) turbulence closure models were used and the results were compared with experimental data from the literature. The validation study showed that both the Standard k-ε and the RNG k-ε model performed better in terms of matching the experimental data and showing consistently faster convergence. Local CHTC along the plate were evaluated with the validated model for different suction flow rates (0.0448 to 0.0688 m/s) and free stream turbulence intensity (0.8% and 20%) at 6 m/s approaching flow velocity with the results showing that the turbulence intensity has a more profound impact on the overall convective heat transfer process. The local CHTC in the solid surface region of the collector remains constant after a certain length for the cases considered; however, the starting length was found to be longer compared to the values reported in previous analytical studies

A State-Space Modeling Approach and Subspace Identification Method for Predictive Control of Multi-Zone Buildings with Mixed-Mode Cooling

The paper presents a control-oriented modeling approach for multi-zone buildings with mixed-mode (MM) cooling that incorporates their mode switching behavior. A forward state-space representation with time-varying system matrices is presented and used for establishing a detailed prediction model of a multi-zone MM building. The linear time-variant state-space (LTV-SS) model, which is considered as a true representation of the building, is used for developing data-driven linear time-invariant state-space models based on the subspace identification algorithm. The simplified black-box model can successfully capture the switching behavior of the MM building with the RMSE of 0.64 ºC

Modeling And Predictive Control Of High Performance Buildings With Distributed Energy Generation And Thermal Storage

Building-integrated photovoltaic-thermal (BIPV/T) systems replace conventional building cladding with solar technology that generates electricity and heat. For example, unglazed transpired solar collectors, known as UTCs, can be integrated with open-loop photovoltaic thermal (PV/T) systems to preheat ventilation air and/or to feed hot air into an air source heat pump, thus satisfying a significant part of the building’s heating and/or hot water requirements while also generating electricity. In the present study a model for a BIPV/T system with a two-stage prototype UTC integrated with PV panels has been developed and used to create a new component in TRNSYS. An open plan office space at Purdue’s Living Lab is used as a test-bed to explore system integration approaches with building HVAC systems and thermal storage mechanisms. The BIPV/T system is coupled with the building through a thermal storage tank, which serves as the heat source, and is connected to the air-to-water heat pump, for the radiant floor heating. The building ventilation system is coupled with the air outlet of the BIPV/T system. A detailed building energy model is developed in TRNSYS, which is used to evaluate the annual performance with the results showing significant energy savings. The objective is to develop models that can be implemented within a predictive control framework for the optimal set-point trajectory of the thermal storage tank. In the MPC formulation, the cost function is the integral of the electric energy consumption over the prediction horizon (48 hrs) subject to thermal comfort and equipment constraints. The study also investigates the impacts of the uncertainty in weather forecast (solar radiation) on MPC performance robustness for the integrated solar system. In our methodology, the TRNSYS model is used as a true representation of the building to identify the parameters of a 3rd order linear time invariant state-space model. The sum of squares minimization was used to identify model parameters that minimize the root-mean-squared error (RMSE) of time series predictions for the three state variables (floor surface temperature of the room; room air temperature; building envelope interior surface temperature) between the reduced order and the TRNSYS model. Known inputs to the system include the ambient temperature, solar radiation (absorbed by the envelope or transmitted through the south-facing glazed façade), internal heat gains (occupancy schedule, equipment, mechanical ventilation and infiltration) and the tank set point temperature. A pattern search optimization algorithm has been used over the training data space to identify the parameter values. Parameter bounds were set to constrain the solution space to physically plausible values. The training and calibration data sets includes 2351 (from Jan 4 to Feb 22, 7 weeks) and 1823 (from Feb 23 to Mar 30) data points, respectively. The simplified 3rd order model shows satisfactory performance with the RMSE for the three state variables within 0.5 °C. Model predictive control relevant identification methods such as 4SID (black-box identification) are also considered and the results will be compared with those using grey-box techniques

Stochastic Model Predictive Control of Mixed-mode Buildings Based on Probabilistic Interactions of Occupants With Window Blinds

Between 4% to 20% of energy used for HVAC, lighting and refrigeration in a building is wasted due to issues associated with systems operations. It is estimated that proper building energy load control and operation can result in up to 40% utility cost savings. Current heuristic rules based on decision trees are difficult to define, manage and optimize as buildings become more complex. Advanced control strategies with weather forecast and cooling load anticipation, known as model predictive control (MPC), offer an attractive alternative for buildings with slow dynamics. However, MPC is mostly practiced through deterministic approaches. Deterministic MPC implicitly assumes that a dynamic model is able to perfectly predict the future behavior of the building over the desired control window, or prediction horizon. However, this assumption is clearly not rational because there will be both modeling errors and disturbances acting on the system over this period. One of these disturbances is associated with building occupant behaviors which interfere with deterministic assumptions. In this study, a probabilistic model of occupants’ behavior on window blind closing event is used to represent the disturbance coming from interactions of building residents with window blinds. This model is a multiple logistic regression analysis, based on a field study in an office building at the University of California, Berkeley (Inkarojrit, 2005). It considers the incident solar radiation on window surface and occupants’ self-reported brightness sensitivity as variable parameters to predict the closing event of blinds with 86.3% of accuracy. The probability of closing event is compared with a random number from the uniform distribution on the interval [0,1] at each time step and if it is greater than the random number, some indicator function will be equal to 1 (closing action) and vice versa. In order to implement the stochastic MPC, Monte Carlo simulation needs to be conducted due to the randomness of occupants’ behavior in closing the blinds. A test-building with mixed-mode cooling and high solar gains is considered as a test-bed. In our methodology, a detailed dynamic building model is developed and it is then used to identify the parameters of a 4th order linear time-variant state-space model. In the MPC formulation, the window opening schedule is optimized for the upcoming prediction horizon and the cost function is the minimization of energy usage subject to thermal comfort constraints during this horizon. Optimal control sequences based on the proposed stochastic MPC framework will be compared with deterministic MPC approaches to investigate possible advantages of considering uncertainties of occupant actions in model predictive controllers of buildings. References: Inkarojrit V., 2005. Balancing Comfort: Occupants’ Control of Window Blinds in Private Offices. PhD thesis, School of Architecture, University of California Berkeley

An investigation of the performance of trickle ventilators

This study presents a full-scale experimental investigation of the air leakage characteristics of two trickle ventilator types, namely slot and pressure-controlled. Furthermore, the performance of trickle ventilators under the influence of the wind, which is the main driving force of the air flow through low-rise buildings, and the validity of the orifice equation to represent their behaviour have been assessed. Finally, the potential of trickle ventilator integration in ventilation design of office buildings is investigated through flow network simulations and design solutions for the opening area of trickle ventilators to satisfy the fresh air requirements, as recommended by ASHRAE, are presented. It was found that the air flow through trickle ventilators may be modelled/predicted accurately and utilized in the design of natural/hybrid ventilation systems. However, the pressure-controlled ventilator appears to be preferable since it provides for better IAQ, and its performance is better than that of the slot ventilator with respect to comfort and energy use to warm up the ventilated air during the heating seaso

Identifying Peer Groups in a Multifamily Residential Building for Eco-Feedback Design

Most residential energy saving strategies require occupants’ participation because they control building and mechanical systems and pay their utility in general. One effective way to increase their participation is to motivate them to change their behaviors by providing relevant information and benefits in their interests. This paper presents baseline energy consumption characteristics in a multifamily housing for eco-feedback design. Although previous studies have proven the energy savings of eco-feedback and smart technology, the results were often mixed or weak because the building, mechanical, geographical, and demographical characteristics were different among houses to make a solid comparison, and the collection of detailed information in residential houses was not available in most cases. Multifamily housing provides a unique opportunity to observe the direct impact of interventions on energy consumption and related behaviors by excluding the effect of building and mechanical characteristics. This paper introduces a non-intrusive experimental setup by using off-the-shelf products to monitor detailed behavior-related information. In addition, we present various classification rules to formalize energy-related behavior such as thermostat-related actions, occupancy detection, and energy normalization. Finally, the use of the collected information is presented, which enables the design of personalized eco-feedback

Airflow prediction in buildings for natural ventilation design : wind tunnel measurements and simulation

Natural/hybrid ventilation systems with motorized operable windows, designed and controlled to utilize the potential for cross-ventilation, represent an area of significant interest in sustainable building design as they can substantially reduce energy consumption for cooling and ventilation. Presently, there is a need for accurate prediction models that can contribute to the improvement of indoor environmental quality and energy performance of buildings, and the increased use of low energy, naturally driven cooling systems. In this regard, the present research aims to enhance airflow prediction accuracy for natural ventilation design of buildings considering advanced experimental and simulation methods. The study considers a Boundary Layer Wind Tunnel (BLWT) approach to investigate the wind-induced driving forces and ventilation flow rates in various building models subject to cross-ventilation. The Particle Image Velocimetry (PIV) technique was used for the first time to evaluate accurately the air velocity field for various cross-ventilation configurations. Detailed measurements were performed to determine mean and fluctuating internal pressures since they affect airflow prediction, occupants' thermal comfort, as well as cladding and structural wind load design of buildings with operable windows. PIV data for the inflow velocity were compared with those by using conventional techniques (e.g., hot-film anemometry) and results show differences, between the two methods, up to a factor of 2.7. This clearly indicates that accuracy can be enhanced with carefully conducted PIV experiments. The study provides guidelines for implementation of cross-ventilation in design practice. These guidelines were developed on the basis of parametric experimental investigations, which quantify the impact of relative inlet-to-outlet size and location on ventilation airflow rates and thermal comfort of building occupants. The study develops a novel simulation methodology combined with a sensitivity analysis focused on modelling issues, such as the impact of zoning assumptions, to predict the envelope pressures and related air-exchange rates in buildings due to wind, stack, and mechanical system effects. An integrated simulation tool (ESP-r) was used to model the airflow/energy interactions in an existing high-rise residential building, and simulation results agree well with monitoring data

A Bayesian Approach for Learning and Predicting Personal Thermal Preference

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 self-tuned. 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 re-estimated based on the field data. The results show evidence of the existence of multi-clusters in people with respect to thermal preference