Data-driven Method for Providing Feedback to Households on Electricity Consumption

被引:0
|
作者
Mononen, Matti [1 ]
Saarenpaa, Jukka [1 ]
Johansson, Markus [1 ]
Niska, Harri [1 ]
机构
[1] Univ Eastern Finland, Dept Environm Sci, Kuopio 70211, Finland
关键词
smart grid; energy efficiency; demand side management; customer behaviour; load profiling; smart metering; energy displays; CUSTOMER CLASSIFICATION; ENERGY;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The building sector a major energy consumer and CO2 emitter, being responsible for approximately 40% of the total consumption in the EU. Active demand side participation of electricity customers is seen as crucial in the management and reduction of the building sector's CO2 emissions. However, today's electricity markets are often lacking strong incentives for active demand side participation. Understandable customer specific comparison information and easy-to-use energy displays can be used to influence customer behaviour and encourage customer participation. This paper presents a data-driven method for producing household level comparison information, based on hourly interval smart meter data and additional household information. Firstly, the customers are segmented by the heating system and the type of housing, followed by weighted clustering that is used to refine the comparison group. In the weighted clustering, normalized load profiles together with properties of the dwelling and the residents are considered, and weights are assigned to the properties according to how much they contribute to the electricity consumption. In this paper, the initial experimental results are presented and discussed, and future development ideas are laid out. The method is under development and testing as a part of the Finnish SGEM-project.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] Research and Implementation of a Carbon Emission Prediction Method Based on Electricity Data-Driven Approach
    Xu, Lianjie
    Pan, Xuewen
    Zhang, Guangya
    Zhang, Renbiao
    Chu, Bei
    Yu, Zhilin
    Zhang, Heng
    Wei, Minjun
    2024 6TH ASIA ENERGY AND ELECTRICAL ENGINEERING SYMPOSIUM, AEEES 2024, 2024, : 1204 - 1209
  • [42] Data-driven structural modeling of electricity price dynamics
    Mahler, Valentin
    Girard, Robin
    Kariniotakis, Georges
    ENERGY ECONOMICS, 2022, 107
  • [43] Data-driven method for predicting energy consumption of machine tool spindle acceleration
    Huang, Binbin
    Jiang, Guozhang
    Yan, Wei
    Jiang, Zhigang
    Lu, Chenxun
    Zhang, Hua
    2021 IEEE 17TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2021, : 528 - 533
  • [44] Data-Driven Prediction Method for Truck Fuel Consumption Based on Car Networking
    Long, Keke
    Wang, Guanqun
    Xu, Zhigang
    Yang, Xiaoguang
    CICTP 2020: TRANSPORTATION EVOLUTION IMPACTING FUTURE MOBILITY, 2020, : 638 - 650
  • [45] Data-Driven Modeling for Energy Consumption Estimation
    Yang, Chunsheng
    Cheng, Qiangqiang
    Lai, Pinhua
    Liu, Jie
    Guo, Hongyu
    EXERGY FOR A BETTER ENVIRONMENT AND IMPROVED SUSTAINABILITY 2: APPLICATIONS, 2018, : 1057 - 1068
  • [46] An Edge Based Data-Driven Chiller Sequencing Framework for HVAC Electricity Consumption Reduction in Commercial Buildings
    Zheng, Zimu
    Chen, Qiong
    Fan, Cheng
    Guan, Nan
    Vishwanath, Arun
    Wang, Dan
    Liu, Fangming
    IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING, 2022, 7 (03): : 487 - 498
  • [47] Nudging electricity consumption using TOU pricing and feedback: evidence from Irish households
    Di Cosmo, Valeria
    O'Hora, Denis
    JOURNAL OF ECONOMIC PSYCHOLOGY, 2017, 61 : 1 - 14
  • [48] A Data-Driven Method for Helping Teachers Improve Feedback in Computer Programming Automated Tutors
    McBroom, Jessica
    Yacef, Kalina
    Koprinska, Irena
    Curran, James R.
    ARTIFICIAL INTELLIGENCE IN EDUCATION, PART I, 2018, 10947 : 324 - 337
  • [49] A Novel Unsupervised Data-Driven Method for Electricity Theft Detection in AMI Using Observer Meters
    Qi, Ruobin
    Zheng, Jun
    Luo, Zhirui
    Li, Qingqing
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
  • [50] Robust data-driven state-feedback design
    Berberich, Julian
    Koch, Anne
    Scherer, Carsten W.
    Allgoewer, Frank
    2020 AMERICAN CONTROL CONFERENCE (ACC), 2020, : 1532 - 1538