以知識圖譜分析建築智慧電表用電行為

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：18

、訪客IP：3.135.195.91

姓名

王乃儀(Nai-Yi Wang) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

以知識圖譜分析建築智慧電表用電行為
(Analyzing Building Smart Meter Electricity Usage Behavior with Knowledge Graphs)

相關論文

★ 路權取得資料探勘與決策輔助工具設計之研究	★ 以時空資料庫管理管線單位道路申挖許可之雛形系統研究
★ 關鍵基礎設施相依性模型設計與應用	★ 應用RFID技術於室內空間防救災時的疏散指引系統之研究
★ 考量列車迴轉與擾動因子情況下高速鐵路系統最佳化排班設計之研究	★ 應用資料探勘分群分類演算法與空間資料庫技術在鋪面裂縫影像辨識之初探
★ 以本體論建構工程程式設計課程之線上考試平台研究	★ 結合遙測影像與GIS資料以資料挖掘技術進行崩塌地辨識-以石門水庫集水區為例
★ 設計整合型手持式行動裝置平台於災害設施損毀資料收集研究	★ 考量擾動因子情況下傳統鐵路時刻表建置合併於高速鐵路時刻表模型之回顧與探討
★ 關鍵基礎設施相依性分析:以竹科某晶圓廠區為例	★ 建築資訊模型於火災原因調查流程的應用
★ Hadoop雲端平台在工程應用之探討研究	★ 關鍵基礎設施投入產出停轉模型之回顧與應用
★ 擴展建築資訊模型於防救災應用：使用Revit平台	★ 應用交通資料蒐集與發佈設備及資料探勘法協助觀光地區交通管理策略之研究：以桃園大溪老街為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本論文的研究聚焦於利用知識圖譜技術分析台灣建築智慧電表中的用電行為，目的在於提升能源管理的效率和準確性，並為用戶和電力公司之間的互動提供支持。台灣作為一個高度工業化的地區，面臨著能源安全的重大挑戰。為了應對這一挑戰，台灣政府積極推動能源轉型政策，包括智慧電網的推動計劃，其中智慧電表系統在提升能源使用效率和透明化方面扮演著關鍵角色。
本研究指出，傳統的關聯式資料庫在處理複雜的用電數據時存在一定的限制，因此提出利用知識圖譜來改善數據分析的效率和準確性。知識圖譜是一種將零散數據結構化並形成可視化知識網絡的技術，能夠揭示數據間的複雜關聯和隱藏模式。在智慧電表的應用場景中，知識圖譜不僅有助於理解能源消費的實際情況，還能指導節能減排政策的制定和實施。本研究的流程包括定義研究目的和問題、進行文獻回顧、建置智慧電表知識圖譜和建築能耗知識圖譜，最後提出結論和建議。文獻回顧部分涉及智慧電表、知識圖譜及其相關應用和技術總覽。通過文獻回顧，研究揭示了知識圖譜在不同領域的應用優勢，為基於智慧電表的知識圖譜架構提供參考，並進行數據分析以提出有針對性的結論和建議。
總結而言，本論文通過探討知識圖譜在智慧電表數據分析中的應用，為能源管理和節能減排提供了新的視角和方法。這不僅有助於提升台灣能源的安全、永續與高效管理，也為能源技術的創新和永續發展貢獻了力量。隨著技術的進步和數據分析能力的提升，知識圖譜將在台灣能源領域發揮越來越重要的作用。

摘要(英)

This thesis focuses on the use of knowledge graph technology to analyze the electricity usage behavior in Taiwanese smart meter data of buildings, aiming to enhance the efficiency and accuracy of energy management, and to support the interaction between users and power companies. Taiwan, being a highly industrialized region, faces a significant challenge in energy security. To address this challenge, the Taiwanese government has been actively promoting energy transition policies, including the advancement of smart grid initiatives, where smart meter systems play a crucial role in improving energy use efficiency and transparency.
The study points out that traditional relational databases have certain limitations in handling complex electricity data. Therefore, it proposes the use of knowledge graphs to improve the efficiency and accuracy of data analysis. Knowledge graphs, a technology that structures scattered data into a meaningful and visualized knowledge network, can reveal complex relationships and hidden patterns in data. In the context of smart meters, knowledge graphs not only help in understanding the actual situation of energy consumption but also guide the formulation and implementation of energy conservation and emission reduction policies. The research process includes defining the research objectives and questions, conducting a literature review, establishing smart meter knowledge graphs and building energy consumption knowledge graphs, and finally presenting conclusions and recommendations. The literature review covers smart meters, knowledge graphs, their related applications, and an overview of knowledge graph technology. Through the literature review, the study highlights the advantages of knowledge graphs in various fields, providing a reference for the structure of knowledge graphs based on smart meters and conducting data analysis to present targeted conclusions and recommendations.
In summary, this thesis explores the application of knowledge graphs in the analysis of smart meter data, offering a new perspective and method for energy management and energy-saving and emission reduction. This not only helps enhance the security, sustainability, and efficient management of energy in Taiwan but also contributes to the innovation and sustainable development of energy technology. As technology advances and data analysis capabilities improve, knowledge graphs are expected to play an increasingly important role in the energy sector in Taiwan.

關鍵字(中)

★ 知識圖譜
★ 智慧電表
★ 建築能耗管理

關鍵字(英)

★ Knowledge Graph
★ Smart Meter
★ Building Energy Consumption Management

論文目次

目錄
摘要 vi
ABSTRACT vii
誌謝 viii
目錄 ix
圖目錄 xi
表目錄 xii
第1章緒論 1
1-1 研究背景與動機 1
1-2 研究問題與目的 2
1-3 研究範圍與限制 3
1-4 研究流程 4
1-5 論文架構 4
第2章文獻回顧 6
2-1 AMI智慧電表基礎建設(Advanced Metering Infrastructure, AMI) 6
2-2 知識圖譜(knowledge graph, KG) 7
2-3 知識圖譜相關應用回顧 8
2-3-1 電池資料管理 9
2-3-2 能源政策分析 11
2-3-3 電網故障診斷 13
2-3-4 建築工程危險識別 15
2-3-5 醫學診斷 18
2-4 知識圖譜技術總覽 20
2-4-1 圖資料庫(Graph Database, GDB) 20
2-4-2 Neo4j 21
2-4-3 GraphXR 24
2-5 文獻回顧總結 24
第3章智慧電表知識圖譜 26
3-1 資料前處理 26
3-2 屬性資料配置 29
3-3 住宅型建築物之耗電情況 29
3-4 知識語義搜索與視覺化 32
3-4-1 語意搜索 32
3-4-2 視覺化 35
第4章建築能耗知識圖譜 39
4-1 圖形數據分析 39
4-2 知識圖譜的介接與規則 44
第5章結論與建議 56
參考文獻 57

參考文獻

參考文獻

經濟部能源署. (2020). 能源轉型白皮書核定本. https://www.moeaea.gov.tw/ECW/populace/content/Content.aspx?menu_id=13178&sub_menu_id=13179
台灣電力公司. (2023). AMI智慧電表資訊網. https://ami-meter.taipower.com.tw/views/home.php
Güngör, V. C., Sahin, D., Kocak, T., Ergüt, S., Buccella, C., Cecati, C., & Hancke, G. P. (2011). Smart grid technologies: Communication technologies and standards [Article]. IEEE Transactions on Industrial Informatics, 7(4), 529-539, Article 6011696. https://doi.org/10.1109/TII.2011.2166794
Hogan, A., Blomqvist, E., Cochez, M., D′Amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A. C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge graphs [Article]. ACM Computing Surveys, 54(4), Article 71. https://doi.org/10.1145/3447772
Paulheim, H. (2017). Knowledge graph refinement: A survey of approaches and evaluation methods [Article]. Semantic Web, 8(3), 489-508. https://doi.org/10.3233/SW-160218
Duan, Y., Shao, L., Hu, G., Zhou, Z., Zou, Q., & Lin, Z. (2017, 7-9 June 2017). Specifying architecture of knowledge graph with data graph, information graph, knowledge graph and wisdom graph. 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA),
Li, X., Lyu, M., Wang, Z., Chen, C. H., & Zheng, P. (2021). Exploiting knowledge graphs in industrial products and services: A survey of key aspects, challenges, and future perspectives [Review]. Computers in Industry, 129, Article 103449. https://doi.org/10.1016/j.compind.2021.103449
Kalaycı, T. E., Bricelj, B., Lah, M., Pichler, F., Scharrer, M. K., & Rubeša-Zrim, J. (2021). A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management. Sustainability, 13(3), 1583. https://doi.org/10.3390/su13031583
Sun, Y., Liu, H., Gao, Y., & Zheng, M. (2023). Research on the Policy Analysis of Sustainable Energy Based on Policy Knowledge Graph Technology—A Case Study in China. Systems, 11(2), 102. https://doi.org/10.3390/systems11020102
喬驥, 王新迎, 閔睿, 白淑華, 姚冬, & 蒲天驕. (2020). 面向電網調度故障處理的知識圖譜框架與關鍵技術初探. 中國電機工程學報, 40(18), 5837-5849.
武霽陽, 李強, 陳潛, 邱有強, 國建寶, & 肖耀輝. (2023). 知識圖譜框架下基于深度學習的HVDC系統故障辨識. 電力系統保護與控制, 51(20), 160-169.
Fang, W., Ma, L., Love, P. E. D., Luo, H., Ding, L., & Zhou, A. (2020). Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology [Article]. Automation in Construction, 119, Article 103310. https://doi.org/10.1016/j.autcon.2020.103310
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017, 22-29 Oct. 2017). Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV),
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., & Sun, J. (2018). Unified Perceptual Parsing for Scene Understanding. In Computer Vision – ECCV 2018 (pp. 432-448). Springer International Publishing. https://doi.org/10.1007/978-3-030-01228-1_26
Kim, K., Kim, H., & Kim, H. (2017). Image-based construction hazard avoidance system using augmented reality in wearable device. Automation in Construction, 83, 390-403. https://doi.org/https://doi.org/10.1016/j.autcon.2017.06.014
Hasan, S. M. S., Rivera, D., Wu, X.-C., Durbin, E. B., Christian, J. B., & Tourassi, G. (2020). Knowledge Graph-Enabled Cancer Data Analytics. IEEE Journal of Biomedical and Health Informatics, 24(7), 1952-1967. https://doi.org/10.1109/jbhi.2020.2990797
Chai, X. (2020). Diagnosis Method of Thyroid Disease Combining Knowledge Graph and Deep Learning. IEEE Access, 8, 149787-149795. https://doi.org/10.1109/access.2020.3016676
Neo4j. (2023). The Neo4j Graph Data Platform. https://neo4j.com/
openCypher. (2023). What is openCypher? https://opencypher.org/
Kinveiz. (2023). GrpahXR. https://www.kineviz.com/graphxr
Wang, R. G., Ho, W. J., Chiang, K. C., Hung, Y. C., Tai, J. K., Tan, J. C., Chuang, M. L., Ke, C. Y., Chien, Y. F., Jeng, A. P., & Chou, C. C. (2023). Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques [Article]. Energies, 16(19), Article 6893. https://doi.org/10.3390/en16196893

Wang, R.G., Wang, N.Y., Tseng, M.C., Tsai, C.H., Chou, C.C. (2024) Applying Large Language Model and Knowledge Graph to the Prediction Work of Sequential Data for Smart Meters, Internal Research Report No. 20240101, National Central University, Jhongli, Taiwan.

指導教授

周建成(Chien-Cheng Chou)

審核日期

2024-1-30

推文