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Abstract: Building managers have specific duties and certain outputs that are required of them. Without the necessary data, information, tools, and time, they are unable to adequately meet their organisational goals. Scenario modelling enables explicit and unambiguous coupling of building functions with other pivotal aspects of building operation in a method that specifically considers the education and technical expertise of building managers. This new method captures, transforms, and communicates the complex interdependencies of environmental and energy management in buildings through an easily navigable, holistic, and reproducible checking mechanism that compares actual performance with predicted performance and completes the “plan-do-check-act” cycle for building managers. Most important, the structured nature of this method caters to the diverse profile of building managers, making it applicable for widespread deployment. This paper demonstrates the benefit of using the new method by examining its application to a performance analysis of two existing buildings. [Copyright &y& Elsevier]

Research into home energy retrofit is important because most existing homes will operate in 2050. A lack of funding or incentives often prevents home energy retrofit, particularly of social housing. This study analysed retrofitted Irish social housing and their gas meter data, including pre-payment meters that require regular “top-ups” purchased from shops. The data comprised records from 100 retrofit and control group homes throughout 2013–2015. A novel evaluation of retrofitted rented homes processed meter data into multiple metrics. Gas consumption is computed per house and weather correction is incorporated, enabling statistical testing of the retrofit. A “difference in difference” technique compared the retrofit and control groups. Gas consumptions of the most popular building type are plotted as distribution curves before and after retrofit. Subsequently the energy use intensity (kWh/m 2 /year) is computed per home; leading to calculation of the prebound effect. In social housing, the prebound effect quantifies energy underconsumption due to self-rationing. Retrofit significantly reduced gas consumption, and reduced its variance among homes. A small positive skewness in the statistical distribution of home gas consumption prevented characterisation as a normal distribution. The prebound effect is high, but alleviated by the retrofit. Finally, retrofit extended average pre-payment intervals. [ABSTRACT FROM AUTHOR]

Commercial buildings during operation are dynamic environments where changes to control strategies and space usage regularly occur. As a result of these and other issues, a performance gap between design intent and actual building performance emerges. This paper seeks to address the operational performance gap and enhance operational building performance through statistically-based fault detection. Additionally, this paper seeks to remedy the knowledge gap building managers face in the identification of key building faults based on minimal quantities and streams of time-series building data. A new methodology is presented that incorporates simulation and breakout detection to address these issues. Residual based exponentially weighted moving average (EWMA) charts and Shewhart charts are compared against a breakout detection algorithm to identify shifts or faults in building performance data. Artificial faults are introduced into the measured time-series data to test the validity of the chosen statistical techniques. Statistical metric sensitivity and precision are calculated to quantify the performance of the new methodology. A summary of results demonstrate that the breakout detection algorithm was the most effective method in detecting meaningful faults in building performance data, followed by residual based EWMA and Shewhart models. [ABSTRACT FROM AUTHOR]

Integrated energy management at both the district and building scales can potentially improve multi-level energy efficiency, but such a solution requires the exchange and analysis of energy performance information from different stakeholders. With the complexities of energy management, there are numerous potential stakeholders and a considerable amount of information to consider. Therefore, a primary challenge is the development of a method that identifies the key stakeholders and extracts key information that supports their performance goals. In this paper, a systematic approach to identify stakeholders and key performance indicators (KPIs) is proposed to draw key information for multi-level energy performance analysis. Firstly, a three-task method for the identification and prioritization of stakeholders is suggested; secondly, a bi-index method to select the KPIs that underpin the stakeholders’ performance goals is defined; finally, the proposed methodology is validated using a case study. The result demonstrates the feasibility of the methodology and illustrates that the selected KPIs contribute to the attainment of key information required to carry out a multi-level energy performance analysis. [ABSTRACT FROM AUTHOR]

Abstract: This paper reviews existing case studies and methods for calibrating whole building energy models to measured data. This research describes a systematic, evidence-based methodology for the calibration of these models. Under this methodology, parameter values in the final calibrated model reference the source of information used to make changes to the initial model. Thus, the final model is based solely on evidence. Version control software stores a complete record of the calibration process, and the evidence on which the final model is based. Future users can review the changes made throughout the calibration process along with the supporting evidence. In addition to the evidence-based methodology, this paper also describes a new zoning process that represents the real building more closely than the typical core and four perimeter zone approach. Though the methodology is intended to apply to detailed calibration studies with high resolution measured data, the primary aspects of the methodology (evidence-based approach, version control, and zone-typing) are independent of the available measured data. [Copyright &y& Elsevier]

Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures. • Utilization of ensemble-based machine learning modeling. • Implementation of an end-use demand segregation. • Identification of key building features for each end-use demand prediction. • Analysis of the impact of retrofit measures using future weather data. [ABSTRACT FROM AUTHOR]

A pronounced gap often exists between expected and actual building performance. The multi-faceted and cross lifecycle causes of this performance gap are found in design assumptions, construction issues and commissioning and operational compromises. Some important factors are firmly rooted in the lack of interoperability around building information. New solutions to the interoperability challenge offer the potential to leverage and reuse available heterogeneous data in a manner that can significantly assist building performance assessment. Linked data provides an open, modular and extensible solution for the challenge. However, in the buildings domain, the integration of rule-based performance metrics and contextual information has yet to be formally established. This paper describes an approach to the provision of in-depth building performance assessment through the integration of OpenMath and linked data. An ontology describing performance metrics in RDF is presented, together with an automated metric evaluation solution using multi-silo queries and computer algebra systems, providing a flexible, automated and extensible mechanism for the assessment of building performance. Building managers and engineers can simultaneously analyse time-series building performance at a range of levels, without burdensome manual intervention such as is the case with traditional solutions. A test implementation on a large university building highlights the potential of this solution. [ABSTRACT FROM AUTHOR]

Developing BEPS models which predict energy usage to a high degree of accuracy can be extremely time consuming. As a result, assumptions are often made regarding the input data required. Making these assumptions without introducing a significant amount of uncertainty to the model can be difficult, and requires experience. Even so, rules of thumb from one geographic region are not automatically transferrable to other regions. This paper develops a methodology which can be used to determine useful guidelines for defining the most influential input data for an accurate BEPS model. Differential sensitivity analysis is carried out on parametric data gathered from five archetype dwelling models. The sensitivity analysis results are used in order to form a guideline minimum set of accurately defined input data. Although the guidelines formed apply specifically to Irish residential dwellings, the methodology and processes used in defining the guidelines is highly repeatable. The guideline minimum data set was applied to practical examples in order to be validated. Existing buildings were modelled, and only the parameters within the minimum data set are accurately defined. All building models predict annual energy usage to within 10% of actual measured data, with seasonal energy profiles well-matching. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the key factors methodology that supports energy managers in determining the optimal building operation strategy in relation to both energy consumption and thermal comfort. The methodology is supported by the utilization of calibrated building energy simulation models that match measured data gathered by an extensive measurement framework. Building management systems, do not allow energy managers to test the impact of changes in control settings of energy systems on energy consumption and thermal comfort. The proposed methodology and prototype tool chain enables energy managers to virtually develop and test the impact of proposed changes to the control settings in the building energy systems prior to their implementation in the physical building. The paper outlines the proposed methodology defining the underpinning concepts and illustrating the performance metrics required to capture the effect of different building operation strategies. A case study is discussed to demonstrate the application of the methodology. [Copyright &y& Elsevier]

• A comprehensive review of urban building energy modeling approaches and methods. • Generalized UBEM frameworks are needed to account for application-specific models. • UBEM domain requires integration with GIS, BIM and cloud-based computing platforms. • Advanced data-driven methods are promising techniques to model building stocks. The world has witnessed a significant population shift to urban areas over the past few decades. Urban areas account for about two-thirds of the world's total primary energy consumption, of which the urban building sector constitutes a significant proportion approximately 40%. Stakeholders such as urban planners and policy makers face substantial challenges when targeting sustainable energy and climate goals related to the buildings' sector, i.e. to reduce energy use and associated emissions. Urban energy modeling is one possible solution that leverages limited resources to estimate building energy use and support appropriate policy formation. Over the past few years, there have been only a few review studies on urban building energy modeling approaches. These studies lack an in-depth discussion of the challenges and future research opportunities related to data-driven, reduced-order, and simulation-based modeling methods. This paper proposes Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of approaches, methods and tools used for urban building energy modeling. Furthermore, this paper proposes a generalized framework based on existing literature for different urban energy modeling methods. The aim of this study is to assist urban planners and energy policymakers when choosing appropriate methods to develop and implement in-depth sustainable building energy planning and analysis projects based on limited available resources. [ABSTRACT FROM AUTHOR]

• A novel method based on clustering, building energy models and Bayesian techniques is presented. • The approach enables the calibration of model parameters for large groups of buildings at an urban scale. • Various data-driven techniques have been tested acting as proxy of the simulation engine. • Three different surrogate structures have been investigated with comparison of the results. • The calibrated models provide realistic results compared to current literature studies. Prediction of building energy use, when performed at urban scale, is influenced by the choice of modelling approach, as well as the quality of available data. In the case of data scarcity, one of the main limitations of current urban scale building energy simulation models, is the use of deterministic approaches to specify modelling inputs for entire classes of buildings. This modelling approach is characterised by three major shortcomings: first, data uncertainties are not comprehensively considered; second, a rigorous method of identification of groups of buildings to be populated by similar modelling parameters is not specified; and third, strategies to calibrate the developed energy models are missing. Considering these challenges, the current paper presents a non-deterministic calibration method for groups of buildings. The methodology utilises four techniques: (i) the use of clustering to identify building groups and associated representative buildings within the urban context; (ii) the application of an automatic building energy modelling approach to simulate buildings within these groups; (iii) the application of data-driven models, used as emulators of the dynamic simulation engine, in a computationally efficient manner; and (iv) the exploitation of a non-deterministic Bayesian calibration framework to identify sets of representative parameters for the building clusters. Datasets, based on 2646 buildings from the city of Geneva, Switzerland, are used as a case study. Validation with measured data, shows that energy consumption and energy intensity predictions when considered at an aggregated scale, are within +/– 5%. On an individual building basis, the validation error is within +/– 20% for approximately 70% of the buildings. [ABSTRACT FROM AUTHOR]

• Evaluation of existing approaches or projects of building archetype. • Developing a generalized multi-scale building archetype methodology. • Implementing the data-driven approaches for the characterization of building archetypes. • Comparison of the modeling results of multi-scale building archetype at BEM and UBEM levels. Globally the building sector accounts for a significant portion of the overall energy demand and greenhouse gas emissions of any country. The most common approach for the collection of modeling and benchmarking data that can be used for predictions of energy performance at a national or urban scale is through classification of the building stock into representative archetypes. Developing such building archetypes is a complex task due to the difficulties associated with gathering detailed geometric and non-geometric data at an urban scale. Although existing databases and projects provide a valuable overview of a building stock, the information about buildings' physical descriptions are not regularly updated. Moreover, these databases cover only the national top-level archetypes and lack crucial information related to city or district scale building stocks. The use of national scale archetypes requires many assumptions that may not hold true for energy modeling at urban or district scale. This paper proposes a multi-scale (national, city, county and district) archetype development methodology using different data-driven approaches. The methodology consists of following five steps: 1) data collection, 2) segmentation, 3) characterization, 4) quantification, and 5) modeling results. We developed a test case based on the available building stock data of Ireland. The test case used previously developed archetype geometries coupled with the parameters determined by the characterization process to calculate annual energy use (kWh) of buildings at a multiple-scales. The resulting archetypes at national, city, county and district scale are analyzed and compared against one another. The results indicate that significant differences occur in terms of energy modeling results when national scale archetypes are used to simulate the energy performance of buildings at the local scale. These multi-scale building archetypes will aid local authorities and city planners when analyzing energy efficiency and consequently, help to improve sustainable energy policy decisions. [ABSTRACT FROM AUTHOR]