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Predicting intra-day load profiles under time-of-use tariffs using smart meter data

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  • Y, Kiguchi
  • Y, Heo
  • M, Weeks
  • R, Choudhary

Abstract

The installation of smart meters enabling electricity load to be measured with half-hourly granularity provides an innovative demand-side management opportunity that is likely to be advantageous for both utility companies and customers. Time-of-use tariffs are widely considered to be the most promising solution for optimising energy consumption in the residential sector. Although there exists a large body of research on demand response in electricity pricing, a practical framework to forecast user adaptation under different Time-of-use tariffs has not been fully developed. The novelty of this work is to provide the first top-down statistical modelling of residential customer demand response following the adoption of a Time-of-use tariff and report the model's accuracy and the feature importance. The importance of statistical moments to capture various lifestyle constraints based on smart meter data, which enables this model to be agnostic about household characteristics, is discussed. 646 households in Ireland during pre/post-intervention of Time-of-use tariff is used for validation. The value of Mean Absolute Percentage Error in forecasting average load for a group of households with the Random Forest method investigated is 2.05% for the weekday and 1.48% for the weekday peak time.

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  • 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.
    • Handle: RePEc:eee:energy:v:173:y:2019:i:c:p:959-970
    DOI: 10.1016/j.energy.2019.01.037
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    References listed on IDEAS

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    1. Faruqui, Ahmad & Sergici, Sanem & Lessem, Neil & Mountain, Dean, 2015. "Impact measurement of tariff changes when experimentation is not an option—A case study of Ontario, Canada," Energy Economics, Elsevier, vol. 52(PA), pages 39-48.
    2. Venkatesan, Naveen & Solanki, Jignesh & Solanki, Sarika Khushalani, 2012. "Residential Demand Response model and impact on voltage profile and losses of an electric distribution network," Applied Energy, Elsevier, vol. 96(C), pages 84-91.
    3. Gils, Hans Christian, 2014. "Assessment of the theoretical demand response potential in Europe," Energy, Elsevier, vol. 67(C), pages 1-18.
    4. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    5. Torriti, Jacopo & Hassan, Mohamed G. & Leach, Matthew, 2010. "Demand response experience in Europe: Policies, programmes and implementation," Energy, Elsevier, vol. 35(4), pages 1575-1583.
    6. Wang, Yong & Li, Lin, 2015. "Time-of-use electricity pricing for industrial customers: A survey of U.S. utilities," Applied Energy, Elsevier, vol. 149(C), pages 89-103.
    7. Cappers, Peter & Goldman, Charles & Kathan, David, 2010. "Demand response in U.S. electricity markets: Empirical evidence," Energy, Elsevier, vol. 35(4), pages 1526-1535.
    8. Tso, Geoffrey K.F & Yau, Kelvin K.W, 2003. "A study of domestic energy usage patterns in Hong Kong," Energy, Elsevier, vol. 28(15), pages 1671-1682.
    9. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    10. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    11. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    12. Ranjan, Manish & Jain, V.K., 1999. "Modelling of electrical energy consumption in Delhi," Energy, Elsevier, vol. 24(4), pages 351-361.
    13. Al-Garni, Ahmed Z. & Zubair, Syed M. & Nizami, Javeed S., 1994. "A regression model for electric-energy-consumption forecasting in Eastern Saudi Arabia," Energy, Elsevier, vol. 19(10), pages 1043-1049.
    14. Torriti, Jacopo, 2012. "Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy," Energy, Elsevier, vol. 44(1), pages 576-583.
    15. Katz, Jonas & Andersen, Frits Møller & Morthorst, Poul Erik, 2016. "Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system," Energy, Elsevier, vol. 115(P3), pages 1602-1616.
    16. Xu, Fang Yuan & Zhang, Tao & Lai, Loi Lei & Zhou, Hao, 2015. "Shifting Boundary for price-based residential demand response and applications," Applied Energy, Elsevier, vol. 146(C), pages 353-370.
    17. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    18. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    19. Ahmad Faruqui & Sanem Sergici, 2010. "Household response to dynamic pricing of electricity: a survey of 15 experiments," Journal of Regulatory Economics, Springer, vol. 38(2), pages 193-225, October.
    Full references (including those not matched with items on IDEAS)

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