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A meta-analytic approach for determining the success factors for energy conservation

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  • Ahir, Rajesh K.
  • Chakraborty, Basab

Abstract

This study aims to identify the factors decisive for realizing energy conservation through data analytics. A meta-analytic based approach is used to present the comprehensive and statistical summary of 40 empirical studies that are based on residential electricity consumption data. A set of inclusion criteria is considered for the selection of studies, and the obtained results were graphically presented using a forest plot. The qualitative analysis of the studies revealed that by appropriately utilizing the crucial factors, namely, ‘comparison with others’, ‘frequency’, ‘mix of other interventions’, ‘energy granularity’, ‘study quality and ‘channel to provide analysis’ are essential for realizing the energy conservation by employing consumption data analytics. The combined effect of the included studies was found to be statistically significant. The strongest and the most favorable association between meta-factors (‘comparison with neighbors’, ‘combination with the goal’, ‘sample size’, ‘data availability’, ‘daily frequency’, ‘appliance-specific analysis’, ‘web portal/mobile app’) and energy conservation was examined. The implications of this study can be employed by electric utilities for better planning of energy conservation activities.

Suggested Citation

  • Ahir, Rajesh K. & Chakraborty, Basab, 2021. "A meta-analytic approach for determining the success factors for energy conservation," Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:energy:v:230:y:2021:i:c:s0360544221010690
    DOI: 10.1016/j.energy.2021.120821
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    as
    1. Choi, Sunghee & Hwang, Seok-Joon & Denzau, Arthur T., 2021. "Do households conserve electricity when they receive signals of greater consumption than neighbours? The Korean case," Energy, Elsevier, vol. 225(C).
    2. Krzysztof Gajowniczek & Tomasz Ząbkowski, 2015. "Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data," Energies, MDPI, vol. 8(7), pages 1-21, July.
    3. Anda, Martin & Temmen, Justin, 2014. "Smart metering for residential energy efficiency: The use of community based social marketing for behavioural change and smart grid introduction," Renewable Energy, Elsevier, vol. 67(C), pages 119-127.
    4. Christoph Flath & David Nicolay & Tobias Conte & Clemens Dinther & Lilia Filipova-Neumann, 2012. "Cluster Analysis of Smart Metering Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 4(1), pages 31-39, February.
    5. Schultz, P. Wesley & Estrada, Mica & Schmitt, Joseph & Sokoloski, Rebecca & Silva-Send, Nilmini, 2015. "Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms," Energy, Elsevier, vol. 90(P1), pages 351-358.
    6. Hargreaves, Tom & Nye, Michael & Burgess, Jacquelin, 2010. "Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors," Energy Policy, Elsevier, vol. 38(10), pages 6111-6119, October.
    7. Ben-Haim, Yakov, 2021. "Feedback for energy conservation: An info-gap approach," Energy, Elsevier, vol. 223(C).
    8. Gans, Will & Alberini, Anna & Longo, Alberto, 2013. "Smart meter devices and the effect of feedback on residential electricity consumption: Evidence from a natural experiment in Northern Ireland," Energy Economics, Elsevier, vol. 36(C), pages 729-743.
    9. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    10. Wang, Zhikun & Crawley, Jenny & Li, Francis G.N. & Lowe, Robert, 2020. "Sizing of district heating systems based on smart meter data: Quantifying the aggregated domestic energy demand and demand diversity in the UK," Energy, Elsevier, vol. 193(C).
    11. Ivanov, Chris & Getachew, Lullit & Fenrick, Steve A. & Vittetoe, Bethany, 2013. "Enabling technologies and energy savings: The case of EnergyWise Smart Meter Pilot of Connexus Energy," Utilities Policy, Elsevier, vol. 26(C), pages 76-84.
    12. Schleich, Joachim & Faure, Corinne & Klobasa, Marian, 2017. "Persistence of the effects of providing feedback alongside smart metering devices on household electricity demand," Energy Policy, Elsevier, vol. 107(C), pages 225-233.
    13. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    14. Venizelou, Venizelos & Philippou, Nikolas & Hadjipanayi, Maria & Makrides, George & Efthymiou, Venizelos & Georghiou, George E., 2018. "Development of a novel time-of-use tariff algorithm for residential prosumer price-based demand side management," Energy, Elsevier, vol. 142(C), pages 633-646.
    15. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    16. Nilsson, Andreas & Bergstad, Cecilia Jakobsson & Thuvander, Liane & Andersson, David & Andersson, Kristin & Meiling, Pär, 2014. "Effects of continuous feedback on households’ electricity consumption: Potentials and barriers," Applied Energy, Elsevier, vol. 122(C), pages 17-23.
    17. Melillo, Andreas & Durrer, Roman & Worlitschek, Jörg & Schütz, Philipp, 2020. "First results of remote building characterisation based on smart meter measurement data," Energy, Elsevier, vol. 200(C).
    18. Lynham, John & Nitta, Kohei & Saijo, Tatsuyoshi & Tarui, Nori, 2016. "Why does real-time information reduce energy consumption?," Energy Economics, Elsevier, vol. 54(C), pages 173-181.
    19. Steg, Linda, 2008. "Promoting household energy conservation," Energy Policy, Elsevier, vol. 36(12), pages 4449-4453, December.
    20. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    21. Yilmaz, S. & Chambers, J. & Patel, M.K., 2019. "Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management," Energy, Elsevier, vol. 180(C), pages 665-677.
    22. Granderson, Jessica & Fernandes, Samuel & Touzani, Samir & Lee, Chih-Cheng & Crowe, Eliot & Sheridan, Margaret, 2020. "Spatio-temporal impacts of a utility’s efficiency portfolio on the distribution grid," Energy, Elsevier, vol. 212(C).
    23. Faruqui, Ahmad & Sergici, Sanem & Sharif, Ahmed, 2010. "The impact of informational feedback on energy consumption—A survey of the experimental evidence," Energy, Elsevier, vol. 35(4), pages 1598-1608.
    24. Imani, Maryam & Ghassemian, Hassan, 2019. "Residential load forecasting using wavelet and collaborative representation transforms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    25. Soltani, Mohammad & Rahmani, Omeid & Ghasimi, Dara S.M. & Ghaderpour, Yousef & Pour, Amin Beiranvand & Misnan, Siti Hajar & Ngah, Ibrahim, 2020. "Impact of household demographic characteristics on energy conservation and carbon dioxide emission: Case from Mahabad city, Iran," Energy, Elsevier, vol. 194(C).
    26. 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.
    27. Chen, Victor L. & Delmas, Magali A. & Locke, Stephen L. & Singh, Amarjeet, 2017. "Information strategies for energy conservation: A field experiment in India," Energy Economics, Elsevier, vol. 68(C), pages 215-227.
    28. Cominola, A. & Giuliani, M. & Piga, D. & Castelletti, A. & Rizzoli, A.E., 2017. "A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring," Applied Energy, Elsevier, vol. 185(P1), pages 331-344.
    29. Carroll, James & Lyons, Seán & Denny, Eleanor, 2014. "Reducing household electricity demand through smart metering: The role of improved information about energy saving," Energy Economics, Elsevier, vol. 45(C), pages 234-243.
    30. Delmas, Magali A. & Fischlein, Miriam & Asensio, Omar I., 2013. "Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012," Energy Policy, Elsevier, vol. 61(C), pages 729-739.
    31. Batalla-Bejerano, Joan & Trujillo-Baute, Elisa & Villa-Arrieta, Manuel, 2020. "Smart meters and consumer behaviour: Insights from the empirical literature," Energy Policy, Elsevier, vol. 144(C).
    32. Yazhou Jiang & Chen-Ching Liu & Yin Xu, 2016. "Smart Distribution Systems," Energies, MDPI, vol. 9(4), pages 1-20, April.
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