HVAC system simulation: overview, issues and some solutions

Integrated performance simulation of buildings’ heating, ventilation and air-conditioning (HVAC) systems can help in reducing energy consumption and increasing occupant comfort. Recognizing this fact, in the last forty years many tools have been developed to help achieving this goal. In this paper we introduce a categorization of these tools with respect to which problems they are meant to deal with and summarize current approaches used for modelling (i) HVAC components, (ii) HVAC control and (iii) HVAC systems in general. Further in this paper, we list issues associated with applications of HVAC modelling and simulation. Finally, we present and discuss co-simulation as one of solutions that can alleviate some of the recognized issues

Using specification and description language for life cycle assesment in buildings

The definition of a Life Cycle Assesment (LCA) for a building or an urban area is a complex task due to the inherent complexity of all the elements that must be considered. Furthermore, a multidisciplinary approach is required due to the different sources of knowledge involved in this project. This multidisciplinary approach makes it necessary to use formal language to fully represent the complexity of the used models. In this paper, we explore the use of Specification and Description Language (SDL) to represent the LCA of a building and residential area. We also introduce a tool that uses this idea to implement an optimization and simulation mechanism to define the optimal solution for the sustainability of a specific building or residential.Peer ReviewedPostprint (published version

Co-simulation for performance prediction of integrated building and HVAC systems - An analysis of solution characteristics using a two-body system

Integrated performance simulation of buildings and heating, ventilation and air-conditioning (HVAC) systems can help in reducing energy consumption and increasing occupant comfort. However, no single building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to analyze integrated building systems and to enable rapid prototyping of innovative building and system technologies. One way to alleviate this problem is to use co-simulation to integrate different BPS tools. Co-simulation approach represents a particular case of simulation scenario where at least two simulators solve coupled differential-algebraic systems of equations and exchange data that couples these equations during the time integration. This article analyzes how co-simulation influences consistency, stability and accuracy of the numerical approximation to the solution. Consistency and zero-stability are studied for a general class of the problem, while a detailed consistency and absolute stability analysis is given for a simple two-body problem. Since the accuracy of the numerical approximation to the solution is reduced in co-simulation, the article concludes by discussing ways for how to improve accuracy

Co-simulation for performance prediction of integrated building and HVAC systems - An analysis of solution characteristics using a two-body system

Integrated performance simulation of buildings and heating, ventilation and airconditioning (HVAC) systems can help reducing energy consumption and increasing occupant comfort. However, no single building performance simulation (BPS) tool offers suffcient capabilities and flexibilities to analyze integrated building systems and to enable rapid prototyping of innovative building and system technologies. One way to alleviate this problem is to use co-simulation to integrate different BPS tools. Co-simulation approach represents a particular case of simulation scenario where at least two simulators solve coupled differential-algebraic systems of equations and exchange data that couples these equations during the time integration. This article analyzes how co-simulation influences consistency, stability and accuracy of the numerical approximation to the solution. Consistency and zero-stability are studied for a general class of the problem, while a detailed consistency and absolute stability analysis is given for a simple two-body problem. Since the accuracy of the numerical approximation to the solution is reduced in co-simulation, the article concludes by discussing ways for how to improve accuracy

Co-simulation for performance prediction of integrated building and HVAC systems - An analysis of solution characteristics using a two-body system

Integrated performance simulation of buildings and heating, ventilation and air-conditioning (HVAC) systems can help in reducing energy consumption and increasing occupant comfort. However, no single building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to analyze integrated building systems and to enable rapid prototyping of innovative building and system technologies. One way to alleviate this problem is to use co-simulation to integrate different BPS tools. Co-simulation approach represents a particular case of simulation scenario where at least two simulators solve coupled differential-algebraic systems of equations and exchange data that couples these equations during the time integration. This article analyzes how co-simulation influences consistency, stability and accuracy of the numerical approximation to the solution. Consistency and zero-stability are studied for a general class of the problem, while a detailed consistency and absolute stability analysis is given for a simple two-body problem. Since the accuracy of the numerical approximation to the solution is reduced in co-simulation, the article concludes by discussing ways for how to improve accuracy

Development of a Detailed Heat Pump Simulation Controls Testbed

In order to increase energy efficiency, it is desirable to reduce power consumption of HVAC systems. One way to reduce power consumption is through careful control of system components. To this end, this work covers the theoretical development of a detailed heat pump testbed for controls. The testbed was created by coupling a whole building simulation model, a detailed heat pump simulation model, an optimization engine, and two data exchange managers. Three component variables were investigated to reduce power: compressor refrigerant flow rate, condenser fan air flow rate, and evaporator air fan flow rate. It was seen that the most impactful variable on capacity and power was the compressor ratio, with the evaporator air fan flow rate having the second greatest impact, and the condenser air fan flow rate the least impact. In addition, an optimizing control that minimizes energy consumption and uses a penalty function to maintain space temperature was added to the testbed. Finally, a number of incremental test cases provide proof of concept for this testbed

Modeling Multiple Occupant Behaviors in Buildings for increased Simulation Accuracy: An Agent-Based Modeling Approach

The dissertation addresses the limitation of current building energy simulation programs in accounting for occupant behaviors, which have been identified as having significant impact on the overall building energy performance. It introduces a new simulation methodology using an agent- based modeling approach that helps to both predict real-world occupant behaviors observed in an operating building and to calculate behavior impact on energy use and occupant comfort. A series of experiments has been conducted using the new methodology and yielded simulation results that not only distinguish themselves from current simulation practices, but also uncover emerging phenomena that enhance the insights on building dynamics

Experiment-Based Validation and Uncertainty Quantification of Partitioned Models: Improving Predictive Capability of Multi-Scale Plasticity Models

Partitioned analysis involves coupling of constituent models that resolve their own scales or physics by exchanging inputs and outputs in an iterative manner. Through partitioning, simulations of complex physical systems are becoming evermore present in scientific modeling, making Verification and Validation of partitioned models for the purpose of quantifying the predictive capability of their simulations increasingly important. Parameterization of the constituent models as well as the coupling interface requires a significant amount of information about the system, which is often imprecisely known. Consequently, uncertainties as well as biases in constituent models and their interface lead to concerns about the accumulation and compensation of these uncertainties and errors during the iterative procedures of partitioned analysis. Furthermore, partitioned analysis relies on the availability of reliable constituent models for each component of a system. When a constituent is unavailable, assumptions must be made to represent the coupling relationship, often through uncertain parameters that are then calibrated. This dissertation contributes to the field of computational modeling by presenting novel methods that take advantage of the transparency of partitioned analysis to compare constituent models with separate-effect experiments (measurements contained to the constituent domain) and coupled models with integral-effect experiments (measurements capturing behavior of the full system). The methods developed herein focus on these two types of experiments seeking to maximize the information that can be gained from each, thus progressing our capability to assess and improve the predictive capability of partitioned models through inverse analysis. The importance of this study stems from the need to make coupled models available for widespread use for predicting the behavior of complex systems with confidence to support decision-making in high-risk scenarios. Methods proposed herein address the challenges currently limiting the predictive capability of coupled models through a focused analysis with available experiments. Bias-corrected partitioned analysis takes advantage of separate-effect experiments to reduce parametric uncertainty and quantify systematic bias at the constituent level followed by an integration of bias-correction to the coupling framework, thus ‘correcting’ the constituent model during coupling iterations and preventing the accumulation of errors due to the final predictions. Model bias is the result of assumptions made in the modeling process, often due to lack of understanding of the underlying physics. Such is the case when a constituent model of a system component is entirely unavailable and cannot be developed due to lack of knowledge. However, if this constituent model were to be available and coupled to existing models of the other system components, bias in the coupled system would be reduced. This dissertation proposes a novel statistical inference method for developing empirical constituent models where integral-effect experiments are used to infer relationships missing from system models. Thus, the proposed inverse analysis may be implemented to infer underlying coupled relationships, not only improving the predictive capability of models by producing empirical constituents to allow for coupling, but also advancing our fundamental understanding of dependencies in the coupled system. Throughout this dissertation, the applicability and feasibility of the proposed methods are demonstrated with advanced multi-scale and multi-physics material models simulating complex material behaviors under extreme loading conditions, thus specifically contributing advancements to the material modeling community

Cost effective and Non-intrusive occupancy detection in residential building through machine learning algorithm

Residential and commercial buildings consume more than 40% of energy and 76% of electricity in the U.S. Buildings also emit more than one-third of U.S. greenhouse gas emissions, which is the largest sector. A significant portion of the energy is wasted by unnecessary operations on heating, ventilation, and air conditioning (HVAC) systems, such as overheating/overcooling or operation without occupants. Wasteful behaviors consume twice the amount of energy compared to energy-conscious behaviors. Many commercial buildings utilize a building management system (BMS) and occupancy sensors to better control and monitor the HVAC and lighting system based on occupancy information. However, the complicated installation process of occupancy sensors and their long payback period have prevented consumers from adopting this technology in the residential sector. Hence, I explored a method to detect the presence of an occupant and utilize it to reduce energy wasting in residential buildings. Existing methods of occupancy detection often focus on directly measure occupancy information from environmental sensors. The validity of such a sensor network highly depends on the room configurations, so the approach is not readily transferrable to other residential buildings. Instead of direct measurement, the proposed scheme detects the change of occupancy in a building. The new scheme implements machine learning methods based on a sequence of human activities that happens in a short period. Since human activities are similar regardless of house floorplan, such an approach may lead to readily transferrable to other residential buildings. I explored three types of human activity sensor to detect door handle touch, water usage, and motion near the entrance, which are highly correlated with the change of occupancy. The occupancy change is not only based on one single human activity, it also depends on a series of human activities that happen in a short period, called event. As the events have different durations and cannot be readily applicable to existing machine learning models due to varying input matrix sizes. Hence, I devised a fixed format to summarize the event regardless of the total duration of the event. Then I used a machine learning model to identify the occupancy change based on the event data. The saving potential of occupancy driven thermostat is about 20 % of energy in residential buildings. However, the actual saving impact in any given house can vary significantly from the average value due to the large variety of residential buildings. Existing building simulation tools did not readily consider the random nature of occupancy and users’ comfort. For this reason, I explored a co-simulation platform that integrates an occupancy simulator, a cooling/heating setpoint control algorithm, a comfort level evaluator, and a building simulator together. I explored the annual energy saving impact of an occupancy-driven thermostat compare with a conventional thermostat. The simulation had been repeated in five U.S. cities (Fairbanks, New York City, San Francisco, Miami, and Phoenix) with distinctive climate zones

A modular co-simulation approach for urban energy systems

Cities are the main site of energy consumption, which result in approximately 71% of global CO2 emissions. Therefore, energy planning in cities can play a critical role in climate change mitigation by improving the efficiency of urban energy usage. The energy characteristics of cities are complex as they involve interactions of multiple domains, such as energy resources, distribution networks, storage and demands from various consumers. Such complexity makes urban energy planning a challenging task, which requires an accurate simulation of the interactions and flows between different urban energy subsystems. Co-simulation has been adopted by a number of researchers to simulate dynamic interactions between subsystems. However, the research has been domain specific and could only be used in limited areas. There was no generic approach to tackle the interoperability challenge of a comprehensive simulation for urban energy systems. To address such a gap, the aim of this thesis is to develop a generic and scalable urban energy co-simulation approach to comprehensively model the dynamic, complex and interactive nature of urban energy systems. This was achieved through the development of a generic and scalable urban energy co-simulation architecture and approach for the integration and orchestration of urban energy simulation tools, also called simulators, from different domains. Nine requirements were identified through a literature review of co-simulation, its approaches, standards, middleware and simulation tools. A conceptual co-simulation architecture was proposed that can address the requirements. The architecture has a modular design with four layers. The simulator layer wraps the simulation tools; the interconnection layer enables the communication between tools programmed in different programming languages; the interoperability layer provides a mechanism for the tool composition and orchestration; and the control layer controls the overall simulation sequence and how data is exchanged. Based on the architecture, a Co-simulation Platform for Ecological-urban (COPE) was developed. Suitable co-simulation software libraries were adopted and mapped together to fulfil the requirements of each layer of COPE to achieve the research objectives. For different simulation purposes, subsystem simulation tools from different domains could be selected and integrated into the platform. A master algorithm could then be developed to orchestrate and synchronise the tools by controlling how the tools are run and how data are exchanged among the tools. In order to evaluate COPE’s fundamental functionality and demonstrate its application, two case studies are presented in the thesis: simulating multiple application domains for a single building and multiple (interacting) buildings respectively. From the case studies, it was observed that COPE can successfully synchronise and manage interactions between the co-simulation platform and integrated simulation tools. The simulation results are validated by comparing the results obtained from the direct coupling approach. The applicability of COPE is demonstrated by simulating energy flows in urban energy systems in a neighbourhood context. Computing performance diagnostics also showed that this functionality is achieved with modest overhead. The layered modular co-simulation approach and COPE presented in this thesis provide a generic and scalable approach to simulating urban energy systems. It could be used for decision making to improve urban energy efficiency