NoSQL Data Storage and Clustering Large Volume of Data from Smart Metering Systems with Impact on Electricity Consumption Peak and Tariff Settings
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio Sánchez-Esguevillas, 2012. "Classification and Clustering of Electricity Demand Patterns in Industrial Parks," Energies, MDPI, vol. 5(12), pages 1-14, December.
- Motlagh, Omid & Paevere, Phillip & Hong, Tang Sai & Grozev, George, 2015. "Analysis of household electricity consumption behaviours: Impact of domestic electricity generation," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 165-178.
- Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
- Kim, Young Se, 2015. "Electricity consumption and economic development: Are countries converging to a common trend?," Energy Economics, Elsevier, vol. 49(C), pages 192-202.
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.- Motlagh, Omid & Berry, Adam & O'Neil, Lachlan, 2019. "Clustering of residential electricity customers using load time series," Applied Energy, Elsevier, vol. 237(C), pages 11-24.
- Rongheng Lin & Budan Wu & Yun Su, 2018. "An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering," Energies, MDPI, vol. 11(9), pages 1-17, September.
- Kwonsik Song & Kyle Anderson & SangHyun Lee & Kaitlin T. Raimi & P. Sol Hart, 2020. "Non-Invasive Behavioral Reference Group Categorization Considering Temporal Granularity and Aggregation Level of Energy Use Data," Energies, MDPI, vol. 13(14), pages 1-21, July.
- Robbert Claeys & Hakim Azaioud & Rémy Cleenwerck & Jos Knockaert & Jan Desmet, 2020. "A Novel Feature Set for Low-Voltage Consumers, Based on the Temporal Dependence of Consumption and Peak Demands," Energies, MDPI, vol. 14(1), pages 1-24, December.
- Song, Kwonsik & Anderson, Kyle & Lee, SangHyun, 2020. "An energy-cyber-physical system for personalized normative messaging interventions: Identification and classification of behavioral reference groups," Applied Energy, Elsevier, vol. 260(C).
- Tang, Rui & Yildiz, Baran & Leong, Philip H.W. & Vassallo, Anthony & Dore, Jonathon, 2019. "Residential battery sizing model using net meter energy data clustering," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
- Song, Kwonsik & Jang, Youjin & Park, Moonseo & Lee, Hyun-Soo & Ahn, Joseph, 2020. "Energy efficiency of end-user groups for personalized HVAC control in multi-zone buildings," Energy, Elsevier, vol. 206(C).
- Jimyung Kang & Jee-Hyong Lee, 2015. "Electricity Customer Clustering Following Experts’ Principle for Demand Response Applications," Energies, MDPI, vol. 8(10), pages 1-24, October.
- Zigui Jiang & Rongheng Lin & Fangchun Yang, 2018. "A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data," Energies, MDPI, vol. 11(9), pages 1-19, August.
- Viegas, Joaquim L. & Vieira, Susana M. & Melício, R. & Mendes, V.M.F. & Sousa, João M.C., 2016. "Classification of new electricity customers based on surveys and smart metering data," Energy, Elsevier, vol. 107(C), pages 804-817.
- 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.
- Zhou, Kaile & Yang, Changhui & Shen, Jianxin, 2017. "Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China," Utilities Policy, Elsevier, vol. 44(C), pages 73-84.
- Sadeghianpourhamami, N. & Demeester, T. & Benoit, D.F. & Strobbe, M. & Develder, C., 2016. "Modeling and analysis of residential flexibility: Timing of white good usage," Applied Energy, Elsevier, vol. 179(C), pages 790-805.
- Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
- Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
- Alejandro Blanco-M. & Karina Gibert & Pere Marti-Puig & Jordi Cusidó & Jordi Solé-Casals, 2018. "Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools," Energies, MDPI, vol. 11(4), pages 1-21, March.
- Wang, Na & Fu, Xiaodong & Wang, Shaobin & Yang, Hao & Li, Zhen, 2022. "Convergence characteristics and distribution patterns of residential electricity consumption in China: An urban-rural gap perspective," Energy, Elsevier, vol. 254(PB).
- Kirikkaleli, Dervis & Sokri, Abderrahmane & Candemir, Mehmet & Ertugrul, Hasan Murat, 2017. "Panel cointegration: Long-run relationship between internet, electricity consumption and economic growth. Evidence from OECD countries," MPRA Paper 114709, University Library of Munich, Germany.
- Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Klaudiusz Borkowski & Elżbieta Jasińska, 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(9), pages 1-19, May.
- Russo, Marianna & Bertsch, Valentin, 2020.
"A looming revolution: Implications of self-generation for the risk exposure of retailers,"
Energy Economics, Elsevier, vol. 92(C).
- Russo, Marianna & Bertsch, Valentin, 2018. "A looming revolution: Implications of self-generation for the risk exposure of retailers," Papers WP597, Economic and Social Research Institute (ESRI).
More about this item
Keywords
clustering; big data; NoSQL; electricity consumption; real-time tariff;All these keywords.
JEL classification:
- L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
- E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
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:ovi:oviste:v:xix:y:2019:i:2:p:327-333. 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: Gheorghiu Gabriela (email available below). General contact details of provider: https://edirc.repec.org/data/feoviro.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.