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The use of hybrid analytics to establish effective strategies for household energy conservation

Author

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  • Gung, Roger R.
  • Huang, Chun-Che
  • Hung, Wen-I
  • Fang, Yu-Jie

Abstract

Taiwan highly relies on foreign countries' supply in various types of energy because of the shortage of energy resources. Taiwanese government encourages industrial and academic research organizations to invest research in energy conservation that could be applicable to countries with similar settings. This research analyzed energy conservation and demographic data of households in Taiwan and developed a novel method for best setting household energy conservation strategies. Through the analysis of high volume and high dimensional data, it showed a hybrid analytical method is required, to predict and identify target households and refine their energy conservation strategies. The hybrid method includes a Logistic Regression based predictive model and a Rough Set Theory-based decision-making model. A hybrid analytical approach is used to identify target households and the critical energy conservation factors by households, such as the use of air conditioning in terms of time length and time of the day, age of the house, energy conservation of the electrical appliances, and price promotion. References show that no study was conducted with such a hybrid approach that can refine strategies for the households that were false-positive in achieving the conservation goal. Besides the above-mentioned purpose, this novel method intended to lower the promotion cost, as promotion is a part of the strategy refinement. In overall, the following problems were considered for decision-making: 1. how to identify the target households that require strategy refinement, 2. how to identify key variables and determine the change of these variables to increase the likelihood of achieving conservation goal, 3. what options can be provided to the decision-makers for feasible strategy refinements. Some studies with qualitative approaches and judgmental decision-making methods were found in this area. However, with the available large dataset, the proposed quantitative method achieved better decision-making efficiency and result optimality.

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  • Gung, Roger R. & Huang, Chun-Che & Hung, Wen-I & Fang, Yu-Jie, 2020. "The use of hybrid analytics to establish effective strategies for household energy conservation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:rensus:v:133:y:2020:i:c:s1364032120305839
    DOI: 10.1016/j.rser.2020.110295
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    1. Sutherland, Ronald J, 1996. "The economics of energy conservation policy," Energy Policy, Elsevier, vol. 24(4), pages 361-370, April.
    2. A. Greening, Lorna & Greene, David L. & Difiglio, Carmen, 2000. "Energy efficiency and consumption -- the rebound effect -- a survey," Energy Policy, Elsevier, vol. 28(6-7), pages 389-401, June.
    3. Hoffman, K. Douglas & Turley, L. W. & Kelley, Scott W., 2002. "Pricing retail services," Journal of Business Research, Elsevier, vol. 55(12), pages 1015-1023, December.
    4. Tegnér, Martin & Ernstsen, Rune Ramsdal & Skajaa, Anders & Poulsen, Rolf, 2017. "Risk-minimisation in electricity markets: Fixed price, unknown consumption," Energy Economics, Elsevier, vol. 68(C), pages 423-439.
    5. Jia, Jun-Jun & Xu, Jin-Hua & Fan, Ying & Ji, Qiang, 2018. "Willingness to accept energy-saving measures and adoption barriers in the residential sector: An empirical analysis in Beijing, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 56-73.
    6. Youn, Hyungho & Jin, Hyun Joung, 2016. "The effects of progressive pricing on household electricity use," Journal of Policy Modeling, Elsevier, vol. 38(6), pages 1078-1088.
    7. Li, Ke & Lin, Boqiang, 2016. "Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model," Applied Energy, Elsevier, vol. 168(C), pages 351-363.
    8. Labandeira, Xavier & Labeaga, José M. & López-Otero, Xiral, 2012. "Estimation of elasticity price of electricity with incomplete information," Energy Economics, Elsevier, vol. 34(3), pages 627-633.
    9. Ali, Ghaffar & Yan, Ningyu & Hussain, Jafar & Xu, Lilai & Huang, Yunfeng & Xu, Su & Cui, Shenghui, 2019. "Quantitative assessment of energy conservation and renewable energy awareness among variant urban communities of Xiamen, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 230-238.
    10. Hu, Junfeng & Kahrl, Fredrich & Yan, Qingyou & Wang, Xiaoya, 2012. "The impact of China's differential electricity pricing policy on power sector CO2 emissions," Energy Policy, Elsevier, vol. 45(C), pages 412-419.
    11. Lim, Kyoung-Min & Lim, Seul-Ye & Yoo, Seung-Hoon, 2014. "Short- and long-run elasticities of electricity demand in the Korean service sector," Energy Policy, Elsevier, vol. 67(C), pages 517-521.
    12. Cohen-Vernik, Dinah & Pazgal, Amit, 2017. "Price Adjustment Policy with Partial Refunds," Journal of Retailing, Elsevier, vol. 93(4), pages 507-526.
    13. Nikolaou, Ioannis E. & Vitouladitis, Haris & Tsagarakis, Konstantinos P., 2012. "The willingness of hoteliers to adopt proactive management practices to face energy issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2988-2993.
    14. Panwar, N.L. & Kaushik, S.C. & Kothari, Surendra, 2011. "Role of renewable energy sources in environmental protection: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1513-1524, April.
    15. Valletti, Tommaso M., 2006. "Differential pricing, parallel trade, and the incentive to invest," Journal of International Economics, Elsevier, vol. 70(1), pages 314-324, September.
    16. Chang, Ching-Ter & Lee, Hsing-Chen, 2016. "Taiwan's renewable energy strategy and energy-intensive industrial policy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 456-465.
    17. Sharma, Sumedha & Dua, Amit & Singh, Mukesh & Kumar, Neeraj & Prakash, Surya, 2018. "Fuzzy rough set based energy management system for self-sustainable smart city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3633-3644.
    18. Benders, Rene M.J. & Kok, Rixt & Moll, Henri C. & Wiersma, Gerwin & Noorman, Klaas Jan, 2006. "New approaches for household energy conservation--In search of personal household energy budgets and energy reduction options," Energy Policy, Elsevier, vol. 34(18), pages 3612-3622, December.
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    2. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).

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