| Author: | Jin, Xiaoyu |
| Title: | City-level building energy data analytics for energy policy investigation toward carbon-neutral buildings |
| Advisors: | Fu, Xiao (BEEE) |
| Degree: | Ph.D. |
| Year: | 2024 |
| Subject: | Buildings -- Energy conservation Buildings -- Environmental aspects Buildings -- Energy conservation -- Data processing Energy policy Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Building Environment and Energy Engineering |
| Pages: | xiii, 210 pages : color illustrations |
| Language: | English |
| Abstract: | In the global trend of carbon neutrality, the building sector shoulders a significant responsibility for decarbonization. City-level building datasets are vital for various applications, from urban building energy modeling to renewable energy analysis, regional energy planning, and building performance assessments. Despite that the global attempts towards data disclosure have started, formidable challenges obstruct its effective use in policymaking and research geared towards achieving carbon neutrality. This study delves into a research question: How can cities properly use city-level building energy open data on policies for carbon neutrality? Firstly, the city-level open datasets of building energy usage are reviewed for this study. This review summarizes a range of open data types, each with its own relevance to policymaking at different levels. For instance, city-wide aggregated data, such as government-published statistical yearbooks, can be employed to analyze overarching and long-term carbon neutrality objectives. Nevertheless, such aggregated datasets often lack in-depth information, exhibiting limitations in terms of temporal and spatial precision. Many cities, particularly those with low levels of data disclosure, rely solely on this type of data. On the other hand, spatial big data, encompassing geographical information and energy performance data for numerous buildings, proves invaluable for assessing specific policy initiatives, including retrofitting, benchmarking, and incentive programs. This type of dataset is characterized by a wealth of data samples and supplementary information, but it presents challenges in terms of data quality. Variability in data standards is common, such as differences in recording building addresses and usage purposes. Moreover, these datasets may exhibit data imbalances, where certain building types dominate while others are underrepresented. In some cases, cities mandate data disclosure only for large buildings, neglecting the substantial portion of small and medium-sized buildings within the urban landscape. Moreover, since the city-level building energy datasets are seldom published for policy research purposes when integrated into energy policy modeling, there are always format issues. And the semantic data of policy is also difficult to integrate into the modeling framework. Therefore, the major challenges and research gaps can be summed up as follows. Firstly, the low levels of data disclosure worldwide hinder policy research. Secondly, the aggregated energy data often lacks detailed information, thereby cannot be used to determine the targeted policies. Thirdly, the spatial big data always have quality issues of data scarcity and imbalance, making the city-level building energy prediction inaccurate. Also, the format issues of building energy and policy data make the policy evaluation process inefficient. To tackle these challenges, the first task of this study is to unlock the potential of open aggregated data to support policymaking, introducing a scenario analysis framework using multi-source data fusion for long-term decarbonization road mapping. The determined roadmap can reveal the significant building and policy types for more detailed policy packages for further policy analysis. Then, this study concentrates on evaluating building energy performance at the city level, a pivotal element for policy upgrades. By addressing the issue of data imbalance, the research aims to swiftly and accurately determine the electricity consumption and energy performance of a multitude of city buildings through the application of data-driven Urban Building Energy Modeling (UBEM) methods rooted in spatial big data. The insights into energy performance status provide guidance for refining the overarching roadmap and indicate precise targets for further policy upgrading. Additionally, this study proposes a UBEM-based policy schema, designed to seamlessly integrate building energy data with policymaking, thereby streamlining the policy impact evaluation process. In this paradigm, policy packages serve as the input for model-based UBEM, enabling the assessment of their impact. These measures are expected to yield significant improvements in the energy performance of buildings, offering a tangible metric for gauging policy effectiveness. This study also develops a platform that functions both as a representation protocol and a tool for the sharing of city-level building energy data. This platform benefits policymakers and researchers by providing access to a broader range of datasets and serving as an exemplar for open-data disclosure. |
| Rights: | All rights reserved |
| Access: | open access |
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