To help solve the climate crisis, municipalities are increasingly modifying their building codes and offering incentives to create greener buildings in their cities. But, city planners find it difficult to set and assess these policies, as most municipalities do not have the types of data used in urban building energy modelling (UBEM) that would allow their planners to forecast the impacts of various building policies. This thesis offers techniques for operating in this data-poor environment, presenting best practices for developing data-driven archetypes with machine learning, demonstrating inference of parameter values to improve archetypes by using surrogate modelling and genetic algorithms, and a demonstration of techniques for assessing residential retrofit impact in a data-limited environment, where data is neither detailed enough to create an in-depth single archetype study, nor broad enough to create an UBEM model. It will be shown that inference techniques have potential, but need a certain amount of detailed data to work, though far less than traditional UBEM techniques. For performing residential retrofit, it will be shown the lack of ideal detailed data does not present an overwhelming obstacle to drawing useful conclusions and that meaningful insight can be extracted despite the lack of precision. Overall, this thesis shows a data-poor environment, while challenging, is a viable environment for both research and policy modelling. / Graduate

京都大学 / 0048 / 新制・論文博士 / 博士(工学) / 乙第12983号 / 論工博第4130号 / 新制||工||1637(附属図書館) / 32453 / 台湾国立成功大学大学院工学研究科建築学専攻 / (主査)教授 鉾井 修一, 教授 原田 和典, 教授 神吉 紀世子 / 学位規則第4条第2項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

Reliable evaluation methods is needed to ensure that investments in energy conservation measures (ECMs) and the construction of new energy efficient buildings lives up to the promised and expected performance. This thesis presents and evaluates a regression method for estimation of influential building parameters: transmission losses above ground (including air leakage), ground heat loss, and overall heat loss coefficient. The analysis is conducted with separately metered electricity, heating and weather data using linear regression models based on the simplified steady-state power balance for a whole building. The evaluation consists of analyzing the robustness of the extracted parameters, their suitability to be used as input values to building energy simulations (BES) tools. In addition, differences between uncalibrated and calibrated BES models are analyzed when they are used to calculate energy savings. Finally the suitability of using a buildings overall heat loss coefficient as a performance verification tool is studied. The presented regression method exhibits high robustness and good agreement with theory. Knowledge of these parameters also proved beneficial in BES calibration procedures as well as in performance verifications. Thus, the presented method shows promising features for reliable energy performance assessments of buildings.

Changes in weather patterns worldwide and global warming increased the demand for high-performance buildings resilient to climate change. Building Performance Simulation (BPS) is a robust technique to test, assess, and enhance energy efficiency measures and comply with stringent energy codes of buildings. Climate has a considerable impact on the buildings' thermal environment and energy performance; therefore, choosing reliable and accurate weather data is crucial for building performance evaluation and reducing the performance gap. Typical Weather Years (TWYs) have been traditionally used for energy simulation of buildings. Even if detailed energy assessments can be performed using available multi-year weather data, most simulations are carried out using a typical single year. As a result, this fictitious year must accurately estimate the typical multi-year conditions. TWYs are widely used because they accelerate the modeling process and cut down on computation time while generating relatively accurate long-term predictions of building energy performance. However, there is no certainty that a single year can describe the changing climate and year-by-year variations in weather patterns. Nowadays, with increased computational power and higher speeds in calculation processes, it is possible to adopt multi-year weather datasets to fully assess long-term building energy performance and avoid errors and inaccuracies during the preliminary selection procedures. This study aims to investigate the impact of Typical Weather Years and Actual Weather Years (AWYs) on a single-family house and a university building under two opposite climates, Winnipeg (cold) and Catania (hot). First, a single-family house in Winnipeg, Canada, was selected to evaluate how typical weather years affect the energy performance of the building and compare it with AWYs simulation. Two widely used typical weather data, CWEC and TMY, were selected for the simulation. The results were compared with the outcomes of simulation using AWYs derived from the same weather station from 2015 to 2019, which covered the latest climate changes. The results showed that typical weather years could not sufficiently capture the year-by-year variation in weather patterns. The typical weather years overestimated the cooling load while underestimating the heating demands compared to the last five actual weather years. A more extensive study was conducted for more confidence in the findings and understanding of the weather files. The research was expanded by comparing the results of building performance simulation of the single-family house and an institutional building with more complex envelope characteristics belonging to the University of Manitoba under cold (Winnipeg, Canada) and hot (Catania, Italy) climates. Overall, 48 simulations were performed using ten actual weather years from 2010 to 2019 and two TWYs from each climate for both buildings. The results showed that while the TWYs either overestimate or underestimate the cooling and heating demands of both buildings, cooling load predictions were highly overestimated in the heating-dominant climate of Winnipeg, ranging from 10.5% to 82.4% for both buildings by CWEC and TMY weather data. In the cooling-dominant climate of Catania, energy simulations using IWEC and TMY typical weather data highly overestimated the heating loads between 2.8% and 82.4%.

Buildings and the built environment account for a significant portion of the global energy use and greenhouse gas emissions, and reducing the energy demand in this sector is crucial for a sustainable energy transition. This highlights the need for accurate and large-scale estimations and predictions of the future energy demand in buildings. Urban building energy modeling (UBEM) is an analytical tool for precise and high-quality energy modelling of city-scale building stocks, which is growing in interest as a useful tool for researchers and decision-makers worldwide. This thesis contributes to the understanding and future development in the field of UBEM and multi-variate cluster analysis. Based on a review of contemporary literature, possible improvements and knowledge gaps regarding UBEM are identified. The majority of UBEM studies are developed for similar applications, and some challenges are close to universal. Difficulties in data acquisition and the identification and characterisation of building archetypes are frequently addressed. Drawing on conclusions from the review, a clustering methodology for identifying building archetypes for hybrid UBEM was developed. The methodology utilised the k-means cluster analysis algorithm for multiple diverse parameters, including socio-economic indicators, and is based on open data sets which eliminates data acquisition issues and allows for easy adaptation. Building archetypes were successfully identified for two large data sets, and proved to be representative of the sample building stock. The results of the analysis also show that the error metric values diverge after a certain number of clusters, for multiple runs of the algorithm. This property of the algorithm in combination with the use of both existing and novel error metrics provide a reliable method for determining the optimal number of clusters. The methodology developed in this thesis enables for an improved modelling process, as a part of a complete UBEM.

This thesis researched the accuracy of measured energy data in comparison to estimated hand calculation data and estimated building energy performance simulation data. In the facility management industry, there is minimal evidence that building energy performance software is being used as a benchmark against measured energy usage within a building. Research was conducted to find examples of measured energy data compared to simulated data. The study examined the accuracy of a simulation software and hand calculations to measured energy data. Data suggests that comparisons may be made between building energy performance simulated data and measured data, though comparisons are solely based on each individual case. Data suggests that heating load simulation data is more accurate for benchmarks than cooling load simulation data. Importing models into Autodesk Green Building Studio (GBS) was not as successful as was expected. When only four of the initial ten building models chosen imported successfully, the remaining twenty-five other building models were imported. Only two of the twenty-five models successfully imported into GBS. The sample size of this research changed from ten to six. The results of this study show that GBS simulated data was close to actual data for the heating loads. For the cooling loads, however, GBS simulated data was consistently low in comparison to the actual data. The results of this study show that hand calculations were consistently low and not as close as GBS simulated data when compared to the actual data for the heating loads. The opposite was true with the cooling loads as hand calculations were consistently high in comparison to actual data.

In traditional planning process, energy analysts work on finalized architectural designs and have limited capability to amend inefficient energy features such as high aspect ratio. Energy efficiency being a major part of sustainable design, the need for performance-oriented design tools has become imminent. There is a wide range of energy simulation tools across the world. Crawley et al. (2005) proposes a plain comparison of the most common ones based on vendor-supplied information. The present report aims to identify simulation tools that can help architects making energy-efficient design decisions in early stage of building process and the most suitable programs will be tested on a standard case in Stockholm area with respect to their architecture, functionalities, usability and limitations.

No description available.

The global prominence of energy-efficient retrofit in the context of aging properties has garnered noteworthy attention. This surge in interest can be attributed to several advantages, encompassing economically viable carbon dioxide (CO₂) emissions reduction, diminished energy expenditures, and improved indoor air quality. Passive retrofits, such as thermal insulation and fenestration improvement, and active retrofits, such as heating setpoint temperature optimization, offer great potential for CO₂ reduction and energy savings. The central objective of this study is ascertaining the feasibility of attaining a 40% reduction in CO₂ emissions with the lowest cost and with constraints on heating setpoints temperature by finding optimal design parameters encompassing thermal insulation (including both single and double-layer), fenestration, and heating setpoint temperatures. This inquiry is substantiated through a case study of the Leblanc residence on the University of Ottawa campus. In pursuit of this objective, a thermal model of the Leblanc building was developed via EnergyPlus and subsequently subjected to a validation process following ASHRAE Guideline 14. After validation, an array of discrete optimization scenarios was executed using the NSGA-II model, facilitated by the JEPLUS+EA software. This approach aimed to identify the most suitable parameters for achieving optimal CO₂ reduction and cost outcomes. Notably, the results showcased 20 solutions, each boasting a reduction of 40% or more in CO₂ emissions and heating setpoint temperature higher than 18 °C. While the choice to prioritize either cost or CO₂ reduction remains at the user's discretion, four solutions have been discerned as the most effective. Furthermore, the findings suggest that implementing these optimal solutions can significantly decrease CO₂ emissions, ranging between 41.79% and 46.36%. The associated costs were also determined to fall within $36,262 to $57,934.

This thesis deals with the energy saving in smart building with focus on the impact of the user behaviour on the energy consumption. The problem of human behaviour modelling has been widely studied in the state of the art, but it is still an open problem in the field of smart building since the stochastic nature of the behaviour is difficult to be accurately represented by numerical tools. An interdisciplinary approach is proposed in order to identify the suitable user features from the psychological and social point of view and to integrate such a representation into a DSS for appliance scheduling and energy cost reduction. The proposed method has exploited location-based features of the users in order to represent their habits and needs and to compute the schedules that maximize the user acceptance toward an “energy-aware” behaviour. The obtained results point out a reduction of the peak-to-average ratio higher than 40% also considering the user constraints imposed by their presence into the building.