Godfried Augenbroe

Quantification methods of technical building performance

A building performance assessment toolkit was developed for use by large corporate owners and building portfolio managers in the US. A variety of technical performance aspects are addressed such as energy, lighting, thermal comfort, maintenance and indoor air quality. Every assessment is based on a normative and objective Performance Indicator (PI). For easy data capture and calculation of PIs, the toolkit was implemented in a web hosted form, enabling facility managers and staff to collect the data during a walk-through enabled by PDA-based data entry. The current set of performance indicators is discussed and the results of the first benchmarks, most notably the energy benchmarks, are reported.

Design analysis integration: supporting the selection of energy saving building components

Abstract This article motivates the need for more research into the interaction between building design and building analysis in a process context. To provide a context for this discussion, the text focusses on a specific problem: the selection of energy saving building components. A strategy to provide (computational) support for their selection is presented; this strategy is then used to discuss the design support provided by current building analysis tools and to assess probable outcomes of current developments. Finally, a new research project revisiting fundamental issues of design analysis integration is presented.

Uncertainty quantification of microclimate variables in building energy models

The last decade has seen a surge in the need for uncertainty analysis (UA) for building energy assessment. The rigorous determination of uncertainty in model parameters is a vital but often overlooked part of UA. To undertake this, one has to turn ones attention to a thriving area in engineering statistics that focuses on uncertainty quantification (UQ) for short. This paper applies dedicated methods and theories that are emerging in this area of statistics to the field of building energy models, and specifically to the microclimate variables embedded in them. We argue that knowing the uncertainty in these variables is a vital prerequisite for ensuing UA of whole building behaviour. Indeed, significant discrepancies have been observed between the predicted and measured state variables of building microclimates. This paper uses a set of approaches from the growing UQ arsenal, mostly regression-based methods, to develop statistical models that quantify the uncertainties in the following most significant microclimate variables: local temperature, wind speed, wind pressure and solar irradiation. These are the microclimate variables used by building energy models to define boundary conditions that encapsulate the interaction of the building with the surrounding physical environment. Although our analysis is generically applicable to any of the current energy models, we will base our UQ examples on the energy model used in EnergyPlus.

Urban Data and Building Energy Modeling: A GIS-Based Urban Building Energy Modeling System Using the Urban-EPC Engine

There is a lack of building energy modeling in current planning support systems (PSS) while building energy efficiency is getting greater attention. This is due to the current limitations of energy modeling at the urban scale and the inconsistency between the available urban data and that required for modeling. The chapter seeks to fill this gap by developing a GIS-based urban building energy modeling system, using the Urban-EPC simulation engine, a modified Energy Performance Calculator engine. This modeling system is compatible with other planning tools, enhanced by the combination of physical and statistical modeling, and adjustable in its resolution, speed and accuracy. Through processing the Data Preparation, Pre-Simulation, Main Simulation and Visualization and Analysis models in this energy modeling system, the urban data related to the basic building information, mutual shading, microclimate and occupant behavior are collected, modified, and synthesized in the GIS platform and then used as the input of the Urban-EPC engine to get energy use of every building in a city, which could be further visualized and analyzed. The method is applied in Manhattan to show its potential as an important component in PSS to inform urban energy policy making.

Quantitative risk management for energy retrofit projects

This article presents a risk analysis method based on Bayesian calibration of building energy models. The Bayesian approach enables probabilistic outputs from the energy model, which are used to quantify risks associated with investing in energy conservation measures in existing buildings. This article demonstrates the applicability of the proposed methodology to support energy saving contracts in the context of the energy service company industry. A case study illustrates the importance of quantifying relative risks by comparing the probabilistic outputs derived from the Bayesian approach with standard practices endorsed by International Performance Measurement and Verification Protocol and ASHRAE guideline 14.

Facility Maintenance Performance Perspective to Target Strategic Organizational Objectives

While facility design is increasingly playing a role addressing strategic organizational objectives, issues pertaining to facility maintenance have typically been left out of the decision-making process. Reasons include the traditional disconnection between facility design and facility maintenance, mainly originating from the lack of a modality to meaningfully represent facility maintenance information during design decision making. This paper discusses two sets of facility maintenance indicators that have the potential to bridge this traditional divide. The objective of this paper is threefold: (1) to illustrate how maintenance performance indicators can support higher-level decision making; (2) to explain how the methodology could be mapped across building sectors; and (3) to show how facility maintenance could play a crucial role in informing strategic decision making. Two types of indicators are introduced based on: (1) normative models in biophysics and physiology and (2) empiricist models of environment-behavior studies. Using examples from hospital, courthouse, and office building design, the paper articulates the manner in which facility performance indicators could be developed and used to support organizational strategic decision making. The paper demonstrates that facility maintenance indicators could be developed for all types of buildings and could be meaningfully represented for consideration during strategic decision making. Moreover, using an example of a healthcare setting, the paper emphasizes how facility maintenance strategies have an impact on higher-level organizational objectives, and vice versa, thereby underscoring the importance to consider maintenance performance during strategic decision making. The paper shows how the two sets of key performance indicators—the hard and the soft—are designed to address different scales of decision making, thereby allowing facility maintenance performance to be considered at all phases of a procurement cycle.

A Design Methodology for Energy Infrastructures at the Campus Scale

To improve the design of large-scale energy infrastructures such as campuses, energy managers need to predict the outcomes of interventions in buildings, as well as have sufficient insights in the implications of changes to the supply and generation network. This article develops a methodology to express overall network energy performance (NEP) with the aim to manage the properties of and multiple relationships between energy consumers and producers in the network. It is based on a directed graph that contains consumers and producers at its nodes, while the connecting arcs represent modes of energy exchange, thus expressing the overall energy topology. The NEP model supports decisions at the generation side, the consumption side, and defines the macroenergy connections, that is, which consumer nodes connect to which suppliers. Our approach forms a bridge between two competing approaches at opposite ends of the spectrum, (1) network models that use high-fidelity dynamic building simulation models but typically break down under the computational weight of hundreds of buildings, and (2) the large scale geographical information system (GIS) approaches that are capable to handle large urban collections of buildings but whose building models are typically too shallow to inspect individual building performance. As an example, the article illustrates the use of the NEP model in the support of systematic improvement of a university campus energy performance.

Decision model for energy performance improvements in existing buildings

Purpose – The purpose of this paper is to describe a tool that supports an investment strategy aimed at improving the energy performance of existing buildings. It is particularly aimed at large building portfolios, such as encountered on university and corporate campuses, where typically a plethora of potential refurbishment interventions are candidates for a greening effort.Design/methodology/approach – The investment optimization strategy is implemented in a web‐based software tool. Under a chosen financial constraint and investment time horizon, the tool empowers campus facility management to make the difficult “greening” decisions as part of their continuous building commissioning. The tool calculates and accepts user data that reflect different types of risks, posed by uncertainties in investment costs, energy performance, and energy cost scenarios. In addition, decision makers (DMs) can set different investment priorities, reflecting their financial risk attitude and commitment to “greenness”.Findin...

Agent-based modeling of interaction between commercial building stocks and power grid

This paper describes a preliminary study on simulating commercial buildings modeled as consumer agents that interact with the power grid. A simple hourly bottom-up building energy model is developed with respect to climate conditions and building design and operation. This model is used to simulate different types of commercial buildings as agents and to derive the hourly load profile of the entire building stock at the city/regional level. By updating building operating parameters in this bottom-up model according to different occupant control strategies under real-time electricity pricing, the total electricity demand of the building stock can be estimated; this will, in turn, affect the electricity market. Two test cases are modeled to estimate the commercial building stock demand response and its impact on the regional electricity market.

Collaborative project planning: A case study of seismic risk analysis using an e-engineering hub

Volatile partnerships are becoming popular and important in the global economy with the involvement of small and medium-sized enterprises (SMEs) and the development of e-business and e-engineering. This requires a new generation of collaborative project planning tools to be built. The e-HUBs (e-engineering enabled by Holonomic and Universal Broker Services) project has conceived and developed a novel approach to e-engineering services. Focusing on project preparation and planning, the e-engineering hub facilitates the outsourcing of engineering services and the fast creation of a project plan that can be executed by engineering teams. This paper presents the e-Hub approach to collaborative project planning and illustrates the key concepts and benefits of this approach through a case study produced as a result of the project. The evaluation results are also discussed.