Uncertainty analysis in building energy assessment has become an active research field because a number of factors influencing energy use in buildings are inherently uncertain. This paper provides a systematic review on the latest research progress of uncertainty analysis in building energy assessment from four perspectives: uncertainty data sources, forward and inverse methods, application of uncertainty analysis, and available software. First, this paper describes the data sources of uncertainty in building performance analysis to provide a firm foundation for specifying variations of uncertainty factors affecting building energy. The next two sections focus on the forward and inverse methods. Forward uncertainty analysis propagates input uncertainty through building energy models to obtain variations of energy use, whereas inverse uncertainty analysis infers unknown input factors through building energy models based on energy data and prior information. For forward analysis, three types of approaches (Monte Carlo, non-sampling, and non-probabilistic) are discussed to provide sufficient choices of uncertainty methods depending on the purpose and specific application of a building project. For inverse analysis, recent research has concentrated more on Bayesian computation because Bayesian inverse methods can make full use of prior information on unknown variables. Fourth, several applications of uncertainty analysis in building energy assessment are discussed, including building stock analysis, HVAC system sizing, variations of sensitivity indicators, and optimization under uncertainty. Moreover, the software for uncertainty analysis is described to provide flexible computational environments for implementing uncertainty methods described in this review. This paper concludes with the trends and recommendations for further research to provide more convenient and robust uncertainty analysis of building energy. Uncertainty analysis has been ready to become the mainstream approach in building energy assessment although a number of issues still need to be addressed.

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A review of uncertainty analysis in building energy assessment

The transition towards energy systems characterized by high share of weather dependent renewable energy sources poses the problem of balancing the mismatch between inflexible production and inelastic demand with appropriate solutions, which should be feasible from the techno-economic as well as from the environmental point of view. Temporal and spatial decoupling of supply and demand is an important element to be considered for the evolution of built environment, especially when creating sectorial level planning strategies and policies. Energy efficiency measures, on-site generation technologies, demand side management and storage systems are reshaping energy infrastructures and energy market, together with innovative business models. Optimal design and operational choices in buildings are systemic, but buildings are also nodes in infrastructural systems and model-based approaches are generally used to guide decision-making processes, at multiple scale. Built environment could represent a suitable intermediate scale of analysis in Multi-Level Perspective planning, collocated among infrastructures and users. Therefore, the spatial and temporal scalability of modelling techniques is analysed, together with the possibility of accommodating multiple stakeholders’ perspectives in decision-making, thereby finding synergies across multiple sectors of energy demand. For this reason, the paper investigates first the cross-sectorial role of models in the energy sector, because the use of common principles and techniques could stimulate a rapid development of multi-disciplinary research, aimed at sustainable energy transitions. Further, relevant issues for the integration of energy storage in built environment are described, considering their relationship with energy efficiency measures, on-site generation and demand side management.

Energy efficiency, demand side management and energy storage technologies – A critical analysis of possible paths of integration in the built environment

Some prior research in office buildings has associated higher indoor temperatures even within the recommended thermal comfort range with increased worker symptoms. We reexamined this relationship in data from 95 office buildings in the U.S. Environmental Protection Agency's Building Assessment Survey and Evaluation (BASE) Study. We investigated relationships between building-related symptoms and thermal metrics constructed from real-time measurements. We estimated odds ratios (ORs) and 95percent confidence intervals in adjusted logistic regression models with general estimating equations, overall and by season. Winter indoor temperatures spanned the recommended winter comfort range; summer temperatures were mostly colder than the recommended summer range. Increasing indoor temperatures, overall, were associated with increases in few symptoms. Higher winter indoor temperatures, however, were associated with increases in all symptoms analyzed. Higher summer temperatures, above 23oC, were associated with decreases in most symptoms. Humidity ratio, a metric of absolute humidity, showed few clear associations. Thus, increased symptoms with higher temperatures within the thermal comfort range were found only in winter. In summer, buildings were overcooled, and only the higher observed temperatures were within the comfort range; these were associated with decreased symptoms. Confirmation of these findings would suggest that thermal management guidelines consider health effects as well as comfort.

/pdf/indoor-thermal-factors-and-symptoms-in-office-workers-57p42j4g0f.pdf

Indoor Thermal Factors and Symptoms in Office Workers: Findings from the U.S. EPA BASE Study

An unprecedented global lockdown has been implemented for controlling the spread of COVID-19 in many countries. These actions are reducing the number of coronics, but with the prolonged COVID-19 outbreak, the restrictions on the activities of people are having a significant impact on all industries. Accordingly, this study aimed to statistically analyze changes in building energy consumption under the COVID-19 pandemic in South Korea, as well as identify the relationship between COVID-19 and building energy consumption according to the building use type. As a result, the average rate of changes in electricity and gas energy consumption decreased −4.46% and −10.35%, respectively, compared to the previous year. The energy consumption in most facilities has tended to decrease while energy consumption in residential facilities increased during COVID-19. The rate of change in building energy consumption had a significantly positive correlation with COVID-19 related factors in various facilities (e.g., neighborhood, religious, educational, and research facilities). Significant findings of this study that social distancing by the COVID-19 outbreak, has changed energy consumption according to building use type indicates the need for new energy systems to effectively manage the energy demand at the community level in the Post COVID-19 era.

Changes in energy consumption according to building use type under COVID-19 pandemic in South Korea

Probabilistic behavioural modeling in building performance simulation—The Brescia eLUX lab

Algorithm for automating the selection of a temperature dependent change point model

In this article, the second in a three-article sequence, the methodology of investigating the capabilities and the limits of hybrid inverse models developed from the shortest monitoring periods possible for a reliable and accurate long-term energy performance prediction in large commercial buildings is presented. Hundreds of thousands of test regression models were created using different lengths of short-term hourly data, ranging from 2 weeks to the entire year. The hourly regression models were created using synthetic data from five different building types and eight different climate zones along with actual monitored data from two buildings. Color area plots enabled detailed analysis of the predictive capability of the hourly hybrid inverse models developed from short-term data sets and conclusions on the necessary length and timing of short-term monitoring were able to be drawn from them. Three application areas for research of this nature were identified: detailed audits, green building performance v...

An hourly hybrid multi-variate change-point inverse model using short-term monitored data for annual prediction of building energy performance, part II: Methodology (1404-RP)

The air temperature distribution in a space with reduced diffuser flow rates and heat loads was studied using simulation. Computational fluid dynamics (CFD) was used to analyze the room air distribution from a side wall diffuser at the design flow rate, and the results were validated with experimental data. CFD was used to predict occupant discomfort under a range of reduced diffuser flow rates. It was found for diffuser flow rates above 30% of the design flow rate that the temperature influence from the jet was minimal. At these flow rates, there was nearly a uniform temperature distribution in the occupied zone. The predicted maximum value of percentage of dissatisfied occupants within the space began to increase for diffuser flow rates below 30% of the design flow rate. The percent dissatisfaction at 1 m room height was greater than 25% for the lowest diffuser flow rate tested (15% of the design flow rate) directly under the diffuser, which was the highest of the test cases, but was 5% or less throughout more than 90% of the room. In contrast, at the higher flow rates, the percent dissatisfied index was 5% or less in only 60%–80% of the room due to increased velocity. Evidence of dumping was already found at the traditional minimum flow rate setting of 30% of design, and so there would be little harm in reducing the minimum flow rate further. Reducing the flow rate below 30% of design just moved the location of the dumping closer to the diffuser. For very low diffuser flow rates (below 30% of the design flow rate), it is recommended that desks be placed away from the supply diffuser to avoid discomfort. Overall, the simulation results indicate that uniform temperatures are maintained in the room at flow rates as low as 15% of design except immediately under the diffuser. This suggests that the VAV minimum flow rates can be set below 30% of design flow as long as the diffuser is at least 1 m from an occupant’s position.

Influence of reduced VAV flow settings on indoor thermal comfort in an office space

Linear change-point models (also known in the building industry as energy-signature models) are commonly used for the monitoring and verification of whole building energy consumption. They have pro...

Mitchell T. Paulus

Papers

Algorithm for automating the selection of a temperature dependent change point model

An hourly hybrid multi-variate change-point inverse model using short-term monitored data for annual prediction of building energy performance, part II: Methodology (1404-RP)

Influence of reduced VAV flow settings on indoor thermal comfort in an office space

Algorithm for explicit solution to the three parameter linear change-point regression model