K-PCD: A new clustering algorithm for building energy consumption time series analysis and predicting model accuracy improvement
Author
Abstract
Suggested Citation
DOI: 10.1016/j.apenergy.2024.124584
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
- Kim, Hakpyeong & Choi, Heeju & Kang, Hyuna & An, Jongbaek & Yeom, Seungkeun & Hong, Taehoon, 2021. "A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
- Xing, Zhuoqun & Pan, Yiqun & Yang, Yiting & Yuan, Xiaolei & Liang, Yumin & Huang, Zhizhong, 2024. "Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction," Applied Energy, Elsevier, vol. 365(C).
- Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
- Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
- Yimei Wang & Yongqian Liu & Li Li & David Infield & Shuang Han, 2018. "Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method," Energies, MDPI, vol. 11(4), pages 1-19, April.
- McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
- Rajabi, Amin & Eskandari, Mohsen & Ghadi, Mojtaba Jabbari & Li, Li & Zhang, Jiangfeng & Siano, Pierluigi, 2020. "A comparative study of clustering techniques for electrical load pattern segmentation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
- Junhwa Hwang & Dongjun Suh & Marc-Oliver Otto, 2020. "Forecasting Electricity Consumption in Commercial Buildings Using a Machine Learning Approach," Energies, MDPI, vol. 13(22), pages 1-29, November.
- Walker, Shalika & Labeodan, Timilehin & Boxem, Gert & Maassen, Wim & Zeiler, Wim, 2018. "An assessment methodology of sustainable energy transition scenarios for realizing energy neutral neighborhoods," Applied Energy, Elsevier, vol. 228(C), pages 2346-2360.
- Kneifel, Joshua & Webb, David, 2016. "Predicting energy performance of a net-zero energy building: A statistical approach," Applied Energy, Elsevier, vol. 178(C), pages 468-483.
- Papadis, Elisa & Tsatsaronis, George, 2020. "Challenges in the decarbonization of the energy sector," Energy, Elsevier, vol. 205(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Qiu, Dawei & Wang, Yi & Wang, Junkai & Jiang, Chuanwen & Strbac, Goran, 2023. "Personalized retail pricing design for smart metering consumers in electricity market," Applied Energy, Elsevier, vol. 348(C).
- Jing, Rui & Li, Yubing & Wang, Meng & Chachuat, Benoit & Lin, Jianyi & Guo, Miao, 2021. "Coupling biogeochemical simulation and mathematical optimisation towards eco-industrial energy systems design," Applied Energy, Elsevier, vol. 290(C).
- Michalakopoulos, Vasilis & Sarmas, Elissaios & Papias, Ioannis & Skaloumpakas, Panagiotis & Marinakis, Vangelis & Doukas, Haris, 2024. "A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs," Applied Energy, Elsevier, vol. 361(C).
- Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
- Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
- Walker, Shalika & Bergkamp, Vince & Yang, Dujuan & van Goch, T.A.J. & Katic, Katarina & Zeiler, Wim, 2021. "Improving energy self-sufficiency of a renovated residential neighborhood with heat pumps by analyzing smart meter data," Energy, Elsevier, vol. 229(C).
- Junhwa Hwang & Dongjun Suh & Marc-Oliver Otto, 2020. "Forecasting Electricity Consumption in Commercial Buildings Using a Machine Learning Approach," Energies, MDPI, vol. 13(22), pages 1-29, November.
- de Guibert, Paul & Shirizadeh, Behrang & Quirion, Philippe, 2020.
"Variable time-step: A method for improving computational tractability for energy system models with long-term storage,"
Energy, Elsevier, vol. 213(C).
- Paul de Guibert & Behrang Shirizadeh & Philippe Quirion, 2020. "Variable time-step: A method for improving computational tractability for energy system models with long-term storage," Post-Print hal-03100309, HAL.
- Arsani Alina & Stefan George, 2024. "Energy Transition and European Sub-Models. Restructuring EU Economy," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 86-101.
- Liu, Pei-chen Barry & Hansen, Mark & Mukherjee, Avijit, 2008. "Scenario-based air traffic flow management: From theory to practice," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 685-702, August.
- Hélène Syed Zwick & S. Ali Shah Syed, 2017. "The polarization impact of the crisis on the Eurozone labour markets: a hierarchical cluster analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 24(7), pages 472-476, April.
- Goethner, Maximilian & Hornuf, Lars & Regner, Tobias, 2021.
"Protecting investors in equity crowdfunding: An empirical analysis of the small investor protection act,"
Technological Forecasting and Social Change, Elsevier, vol. 162(C).
- Maximilian Goethner & Lars Hornuf & Tobias Regner, 2020. "Protecting Investors in Equity Crowdfunding: An Empirical Analysis of the Small Investor Protection Act," CESifo Working Paper Series 8351, CESifo.
- Maximilian Goethner & Lars Hornuf & Tobias Regner, 2020. "Protecting Investors in Equity Crowdfunding: An Empirical Analysis of the Small Investor Protection Act," Jena Economics Research Papers 2020-009, Friedrich-Schiller-University Jena.
- Al-Jabri, Hareb & Das, Probir & Khan, Shoyeb & AbdulQuadir, Mohammad & Thaher, Mehmoud Ibrahim & Hoekman, Kent & Hawari, Alaa H., 2022. "A comparison of bio-crude oil production from five marine microalgae – Using life cycle analysis," Energy, Elsevier, vol. 251(C).
- Łukasz Jarosław Kozar & Robert Matusiak & Marta Paduszyńska & Adam Sulich, 2022. "Green Jobs in the EU Renewable Energy Sector: Quantile Regression Approach," Energies, MDPI, vol. 15(18), pages 1-21, September.
- Jānis Krūmiņš & Māris Kļaviņš, 2023. "Investigating the Potential of Nuclear Energy in Achieving a Carbon-Free Energy Future," Energies, MDPI, vol. 16(9), pages 1-31, April.
- Pennings, J.S.J. & van Kranenburg, H.L. & Hagedoorn, J., 2005. "Past, present and future of the telecommunications industry," Research Memorandum 016, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- Akintayo T. Abolude & Wen Zhou, 2018. "A Comparative Computational Fluid Dynamic Study on the Effects of Terrain Type on Hub-Height Wind Aerodynamic Properties," Energies, MDPI, vol. 12(1), pages 1-14, December.
- Li-Xuan Qin & Steven G. Self, 2006. "The Clustering of Regression Models Method with Applications in Gene Expression Data," Biometrics, The International Biometric Society, vol. 62(2), pages 526-533, June.
- Sumitkumar, Rathor & Al-Sumaiti, Ameena Saad, 2024. "Shared autonomous electric vehicle: Towards social economy of energy and mobility from power-transportation nexus perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
- Xavier Serrano-Guerrero & Guillermo Escrivá-Escrivá & Santiago Luna-Romero & Jean-Michel Clairand, 2020. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles," Energies, MDPI, vol. 13(5), pages 1-23, February.
More about this item
Keywords
Building energy consumption; Time series; Clustering algorithms; Machine learning; Prediction;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:377:y:2025:i:pc:s0306261924019676. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.