Modeling and control of complex building energy systems

Building energy sector is one of the important sources of energy consumption and especially in the United States, it accounts for approximately 40% of the total energy consumption. Besides energy consumption, it also contributes to CO2 emissions due to the combustion of fossil fuels for building operation. Preventive measures have to be taken in order to limit the greenhouse gas emission and meet the increasing load demand, energy efficiency and savings have been the primary objective globally. Heating, Ventilation, and air-conditioning (HVAC) system is a major source of energy consumption in buildings and is the principal building system of interest. These energy systems comprising of many subsystems with local information and heterogeneous preferences demand the need for coordination in order to perform optimally. The performance required by a typical airside HVAC system involving a large number of zones are multifaceted, involves attainment of various objectives (such as optimal supply air temperature) which requires coordination among zones. The required performance demands the need for accurate models (especially zones), control design at the individual (local-VAV (Variable Air Volume)) subsystems and a supervisory control (AHU (Air Handling Unit) level) to coordinate the individual controllers. In this thesis, an airside HVAC system is studied and the following considerations are addressed: a) A comparative evaluation among representative methods of different classes of models, such as physics-based (e.g., lumped parameter autoregressive models using simple physical relationships), data-driven (e.g., artificial neural networks, Gaussian processes) and hybrid (e.g., semi-parametric) methods for different physical zone locations; b) A framework for control of building HVAC systems using a methodology based on power shaping paradigm that exploits the passivity property of a system. The system dynamics are expressed in the Brayton-Moser (BM) form which exhibits a gradient structure with the mixed-potential function, which has the units of power. The power shaping technique is used to synthesize the controller by assigning a desired power function to the closed loop dynamics so as to make the equilibrium point asymptotically stable, and c) The BM framework and the passivity tool are further utilized for stability analysis of constrained optimization dynamics using the compositional property of passivity, illustrated with energy management problem in buildings. Also, distributed optimization (such as subgradient) techniques are used to generate the optimal setpoints for the individual local controllers and this framework is realized on a distributed control platform VOLTTRON, developed by the Pacific Northwest National Laboratory (PNNL)

A Transfer Operator Methodology for Optimal Sensor Placement Accounting for Uncertainty

Sensors in buildings are used for a wide variety of applications such as monitoring air quality, contaminants, indoor temperature, and relative humidity. These are used for accessing and ensuring indoor air quality, and also for ensuring safety in the event of chemical and biological attacks. It follows that optimal placement of sensors become important to accurately monitor contaminant levels in the indoor environment. However, contaminant transport inside the indoor environment is governed by the indoor flow conditions which are affected by various uncertainties associated with the building systems including occupancy and boundary fluxes. Therefore, it is important to account for all associated uncertainties while designing the sensor layout. The transfer operator based framework provides an effective way to identify optimal placement of sensors. Previous work has been limited to sensor placements under deterministic scenarios. In this work we extend the transfer operator based approach for optimal sensor placement while accounting for building systems uncertainties. The methodology provides a probabilistic metric to gauge coverage under uncertain conditions. We illustrate the capabilities of the framework with examples exhibiting boundary flux uncertainty

Effectiveness of CFD simulation for the performance prediction of phase change building boards in the thermal environment control of indoor spaces

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2013 ElsevierThis paper reports on a validation study of CFD models used to predict the effect of PCM clay boards on the control of indoor environments, in ventilated and non-ventilated situations. Unlike multi-zonal models, CFD is important in situations where localised properties are essential such as in buildings with complex and large geometries. The employed phase change model considers temperature/enthalpy hysteresis and varying enthalpy-temperature characteristics to more accurately simulate the phase change behaviour of the PCM boards compared to the standard default modelling approach in the commercial CFD codes. Successful validation was obtained with a mean error of 1.0 K relative to experimental data, and the results show that in addition to providing satisfactory quantitative results, CFD also provides qualitative results which are useful in the effective design of indoor thermal environment control systems utilising PCM. These results include: i) temperature and air flow distribution within the space resulting from the use of PCM boards and different night ventilation rates; ii) the fraction of PCM experiencing phase change and is effective in the control of the indoor thermal environment, enabling optimisation of the location of the boards; and iii) the energy impact of PCM boards and adequate ventilation configurations for effective night charging.This work was funded through sponsorship from the UK Engineering and Physical Sciences Research Council (EPSRC), Grant No: EP/H004181/1

Green Design Studio: A modular-based approach for high-performance building design

Building energy and indoor air quality (IAQ) are of great importance to climate change and people’s health and wellbeing. They also play a key role in mitigating the risk of transmissions of infectious diseases such as COVID-19. Building design with high performance in energy efficiency and IAQ improvement can save energy, reduce carbon emissions, and improve human health. High-performance building (HPB) design at the early design stage is critical to building’s real performance during operation. Fast and reliable prediction of building performance is, therefore, required for HPB design during the early design iterations. A modular-based method to analyze building performance on energy efficiency, thermal comfort, IAQ, health impacts, and infection risks was developed, implemented, and demonstrated in this study. The modular approach groups the building technologies and systems to modules that can be analyzed at multi-scale building environments, from urban scale, to building, room, and personal scale. The proposed approach was implemented as a plugin on Rhino Grasshopper, a 3D architectural geometry modeling tool. The design and simulation platform was named Green Design Studio. Reduced-order physics-based models were used to simulate thermal, air, and mass transfer and storage in the buildings. Three cases were used as the study case to demonstrate the module-based approach and develop the simulation platform. Optimization algorithms were applied to optimize the design and settings of the building modules beyond the reference case. The case study shows that the optimal design of the small office determined by the developed platform can save up to 27.8% energy use while mitigating more than 99% infection risk compared to the reference case. It reveals that the optimization of green building design using the proposed approach has high potential of energy saving and IAQ improvement. In support of the application of the Green Design Studio platform, a database of green building technology modules for energy efficiency and IAQ improvement was created. Two selected emerging IAQ strategies were studied using the proposed approach and the developed tool, including the in-duct needlepoint bipolar ionizer and the combination of displacement ventilation and partitions. The in-duct ionization system can provide an equivalent single pass removal efficiency (SPRE) of 3.8-13.6% on particle removal without significant ozone and volatile organic compounds (VOCs) removal and generation with minimal energy use. The combined application of displacement ventilation and desk partitions can also effectively mitigate potential virus transmission through coughing or talking. The abundant performance data from experiments and detailed simulations for the studied technologies will be used by the database of the green building technologies and systems. It will allow these two technologies to be applied through the Green Design Studio approach during the early-design stage for a high-performance building. This can potentially help to address IAQ issues, particularly the airborne transmission of respiratory diseases, while maintaining high energy efficiency

Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

INTELLIGENT DEMAND SIDE MANAGEMENT OF RESIDENTIAL BUILDING ENERGY SYSTEMS

Building energy performance has emerged as a major issue in recent years due to growing concerns over costs, resource limitations, and the potential impact on climate. According to the 2011 Buildings Energy Data Book (prepared by D&R International, Ltd. for the US Department of Energy, March 2012), the built environment demands about 41% of primary energy in the United States [1]. Given the emergence of modern sensing technologies and low-cost data-processing capabilities, there is a growing interest in better managing and controlling consumption within buildings. Estimates suggest that the simple act of continuous monitoring can lead to improvements on the order of 20% [118]. To further reduce and better control energy consumption, one can turn to the use of real-time energy-performance modeling. This thesis adopts the view that smaller buildings (i.e. homes and smaller commercial facilities), which represent more than half of the sector’s consumption, provide a rich opportunity for low-cost monitoring solutions. The real advantage lies in the growth of so-called smart meters for demand monitoring and advanced sensing for improved load control. In particular, this thesis considers the use of a small sensor package for the creation of autonomously developed, data-driven thermal models. Such models can be used to predict and control the consumption of space heating and cooling equipment, which currently represent about 50% of residential consumption in the United States. The key contribution of this work is the real-time identification of thermal parameters in homes using only two temperature sensors, solar irradiance measurements, and a power meter with load-tracking capabilities. The proposed identification technique uses the Prediction Error Method (PEM) to find a Multiple Input Single Output (MISO) state-space model. Two different models have been devised, and both have been field tested. The first focuses on energy forecasting and enables various diagnostic features; the other is formulated for more advanced capacity controls in vapor-compression air conditioners. A Model Predictive Control (MPC) scheme has been implemented and shown in simulation to yield energy reductions on the order of 30% over typical thermostatic control schemes. Similarly, the diagnostic model has been used to detect capacity degradation in operational units