Insights into demand-side management with big data analytics in electricity consumers' behaviour
暂无分享,去创建一个
Simona Vasilica Oprea | Adela Bâra | Mihai Alexandru Botezatu | Bogdan George Tudorica | Maria Irène Calinoiu | A. Bâra | S. Oprea | M. Botezatu | B. Tudorică | Maria Calinoiu
[1] Sean Lyons,et al. Reducing household electricity demand through smart metering: The role of improved information about energy saving , 2014 .
[2] Miriam Fischlein,et al. Information Strategies and Energy Conservation Behavior: A Meta-Analysis of Experimental Studies from 1975 to 2012 , 2013 .
[3] Shanlin Yang,et al. Household monthly electricity consumption pattern mining: A fuzzy clustering-based model and a case study , 2017 .
[4] Changhui Yang,et al. Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China , 2017 .
[5] Mario Callegaro,et al. The Role of Surveys in the Era of “Big Data” , 2018 .
[6] Nasseh Tabrizi,et al. A Survey on Real-Time Big Data Analytics: Applications and Tools , 2016, 2016 International Conference on Computational Science and Computational Intelligence (CSCI).
[7] Alain Barrat,et al. Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys , 2015, PloS one.
[8] Yi Wang,et al. Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges , 2018, IEEE Transactions on Smart Grid.
[9] Houda Daki,et al. Big Data management in smart grid: concepts, requirements and implementation , 2017, Journal of Big Data.
[10] Murtaza Haider,et al. Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..
[11] Z. Irani,et al. Critical analysis of Big Data challenges and analytical methods , 2017 .
[12] Yi Wang,et al. Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications , 2016, IEEE Transactions on Smart Grid.
[13] C. L. Philip Chen,et al. A Data-Emergency-Aware Scheduling Scheme for Internet of Things in Smart Cities , 2018, IEEE Transactions on Industrial Informatics.
[14] GandomiAmir,et al. Beyond the hype , 2015 .
[15] Mohamed Salah Gouider,et al. Big data analysis to Features Opinions Extraction of customer , 2017, KES.
[16] Shanlin Yang,et al. Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies , 2018 .
[17] Michelangelo Ceci,et al. DENCAST: distributed density-based clustering for multi-target regression , 2019, Journal of Big Data.
[18] Lanlan Li,et al. Compression of smart meter big data: A survey , 2018, Renewable and Sustainable Energy Reviews.
[19] Riccardo Russo,et al. The question of energy reduction: The problem(s) with feedback , 2015 .
[20] Marleen Huysman,et al. Debating big data: A literature review on realizing value from big data , 2017, J. Strateg. Inf. Syst..
[21] Simona Vasilica Oprea,et al. Sliding Time Window Electricity Consumption Optimization Algorithm for Communities in the Context of Big Data Processing , 2019, IEEE Access.
[22] Francisco Martínez-Álvarez,et al. Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities , 2018 .
[23] Peter A. Flach,et al. A Big Data platform for smart meter data analytics , 2019, Comput. Ind..
[24] Nobuo Sato,et al. Measuring Happiness Using Wearable Technology — Technology for Boosting Productivity in Knowledge Work and Service Businesses — , 2015 .
[25] Matthew E. Kahn,et al. Energy Conservation "Nudges" and Environmentalist Ideology: Evidence from a Randomized Residential Electricity Field Experiment , 2010 .