Research on user electricity consumption behavior and energy consumption modeling in big data environment

2021 ◽  
Author(s):  
Ma Chuang ◽  
Wang Yikuai ◽  
Zhang Junda ◽  
Chen Ke ◽  
Gong Feixiang ◽  
...  
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Electricity Consumption ◽  
Consumption Behavior ◽  
Data Environment ◽  
Energy Consumption Modeling

scholarly journals Analysis of User Energy Consumption Patterns Based on Data Mining

2020 ◽  
Vol 213 ◽  
pp. 02040
Author(s):  
Weitao Liu ◽  
Fuqing Wang ◽  
Hang Shi ◽  
Yan Zhang ◽  
Ruobo Chen
Keyword(s):  
Energy Consumption ◽  
Behavior Analysis ◽  
Energy Use ◽  
Electricity Consumption ◽  
Energy System ◽  
Management Service ◽  
Consumption Behavior ◽  
User Classification ◽  
Integrated Energy System ◽  
Use Behavior

The energy use behavior analysis method can dig out the user’s energy use behavior rules from the energy use big data, thereby improving the quality of the grid-side management service in the integrated energy system. Firstly, it summarizes the characteristics of the integrated energy system and constructs the integrated energy system service system; secondly, it summarizes the data-driven electricity consumption behavior analysis research model. Then, it elaborates on the collection and aggregation of electricity consumption information, and refined user classification. Next, the comprehensive application of energy consumption behavior analysis in load forecasting, demand response modeling and other typical scenarios is deeply analyzed. Finally, the challenges that may be encountered in further research are clarified and the follow-up work is prospected.

scholarly journals Modeling Residential Energy Consumption

2021 ◽  
Vol 29 (2) ◽  
pp. 166-193
Author(s):  
Roya Gholami ◽  
Rohit Nishant ◽  
Ali Emrouznejad
Keyword(s):  
Energy Consumption ◽  
Service Providers ◽  
Electricity Consumption ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Smart Meters ◽  
Household Energy ◽  
Consumption Behavior ◽  
Data Mining Approach ◽  
Residential Characteristics

Smart meters that allow information to flow between users and utility service providers are expected to foster intelligent energy consumption. Previous studies focusing on demand-side management have been predominantly restricted to factors that utilities can manage and manipulate, but have ignored factors specific to residential characteristics. They also often presume that households consume similar amounts of energy and electricity. To fill these gaps in literature, the authors investigate two research questions: (RQ1) Does a data mining approach outperform traditional statistical approaches for modelling residential energy consumption? (RQ2) What factors influence household energy consumption? They identify household clusters to explore the underlying factors central to understanding electricity consumption behavior. Different clusters carry specific contextual nuances needed for fully understanding consumption behavior. The findings indicate electricity can be distributed according to the needs of six distinct clusters and that utilities can use analytics to identify load profiles for greater energy efficiency.

Big Data Environment and Humanities Platform

2017 ◽  
Vol 39 (5) ◽  
pp. 177-202
Author(s):  
Hyun-Cheol Choi
Keyword(s):  
Big Data ◽  
Data Environment
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