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A hierarchical Bayesian regression model for predicting summer residential electricity demand across the U.S.A

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  • Wang, Siyan
  • Sun, Xun
  • Lall, Upmanu

Abstract

A hierarchical Bayesian model is developed for predicting monthly residential per capita electricity consumption at the state level across the USA. The summer period is selected to target cooling requirements that are generally directly associated with electricity use compared with winter heating requirements that are derived from a mix of energy sources that has changed over time. Historical monthly electricity consumption data from 1990 to 2013 are used to build a predictive model with a set of corresponding climate and non-climate covariates. A clustering analysis was performed first to identify groups of states that had similar temporal patterns for the cooling-degree-days of each state. Then, a partial pooling model is applied to each cluster to assess the sensitivity of monthly per capita residential electricity demand to each predictor (these are cooling-degree-days, gross domestic product (GDP) per capita, per capita electricity demand from the previous month and previous year, and the residential electricity price). Most of the predictors are significant for most of the states across USA. The sensitivity of residential electricity demand to cooling-degree-days has an identifiable geographic distribution with a stronger relationship in northeastern United States. This predictive model considers interactions between causal factors at large spatial scales and quantifies the model uncertainty. This study can be applied as guide in the future energy management.

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  • Wang, Siyan & Sun, Xun & Lall, Upmanu, 2017. "A hierarchical Bayesian regression model for predicting summer residential electricity demand across the U.S.A," Energy, Elsevier, vol. 140(P1), pages 601-611.
    • Handle: RePEc:eee:energy:v:140:y:2017:i:p1:p:601-611
    DOI: 10.1016/j.energy.2017.08.076
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    1. Zarnikau, Jay, 2003. "Functional forms in energy demand modeling," Energy Economics, Elsevier, vol. 25(6), pages 603-613, November.
    2. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
    3. Isaac, Morna & van Vuuren, Detlef P., 2009. "Modeling global residential sector energy demand for heating and air conditioning in the context of climate change," Energy Policy, Elsevier, vol. 37(2), pages 507-521, February.
    4. Büyükalaca, Orhan & Bulut, Hüsamettin & YIlmaz, Tuncay, 2001. "Analysis of variable-base heating and cooling degree-days for Turkey," Applied Energy, Elsevier, vol. 69(4), pages 269-283, August.
    5. Paul, Anthony & Myers, Erica & Palmer, Karen, 2009. "A Partial Adjustment Model of U.S. Electricity Demand by Region, Season, and Sector," RFF Working Paper Series dp-08-50, Resources for the Future.
    6. Pardo, Angel & Meneu, Vicente & Valor, Enric, 2002. "Temperature and seasonality influences on Spanish electricity load," Energy Economics, Elsevier, vol. 24(1), pages 55-70, January.
    7. Mestekemper, Thomas & Kauermann, Göran & Smith, Michael S., 2013. "A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting," International Journal of Forecasting, Elsevier, vol. 29(1), pages 1-12.
    8. Maddala, G S, et al, 1997. "Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 90-100, January.
    9. Serra, Teresa, 2011. "Volatility spillovers between food and energy markets: A semiparametric approach," Energy Economics, Elsevier, vol. 33(6), pages 1155-1164.
    10. Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
    11. Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.
    12. Nguyen, Hang T. & Nabney, Ian T., 2010. "Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models," Energy, Elsevier, vol. 35(9), pages 3674-3685.
    13. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
    14. Krishnamurthy, Chandra Kiran B. & Kriström, Bengt, 2015. "A cross-country analysis of residential electricity demand in 11 OECD-countries," Resource and Energy Economics, Elsevier, vol. 39(C), pages 68-88.
    15. Xiao, Ni & Zarnikau, Jay & Damien, Paul, 2007. "Testing functional forms in energy modeling: An application of the Bayesian approach to U.S. electricity demand," Energy Economics, Elsevier, vol. 29(2), pages 158-166, March.
    16. Jihoon Min & Zeke Hausfather & Qi Feng Lin, 2010. "A High‐Resolution Statistical Model of Residential Energy End Use Characteristics for the United States," Journal of Industrial Ecology, Yale University, vol. 14(5), pages 791-807, October.
    17. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2002. "Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption," Economics Letters, Elsevier, vol. 76(3), pages 375-382, August.
    18. 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.
    19. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
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