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A Bayesian Approach to Combining Conditional Demand and Engineering Models of Electricity Usage

Author

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  • Herriges, Joseph A.
  • Caves, Douglas W.
  • Train, K.
  • Windle, R. J.

Abstract

Load forecasting models employed in the electric utility industry have become increas ingly dependent upon information about the electricity used by indivi dual appliances (i.e., end uses). Currently, information on appliance usage is obtained from two fundamentally different sources: (1) engi neering estimates and (2) conditional demand estimates. Bayesian anal ysis provides the means by which these two sources can be formally co mbined. Observed usage data (via the conditional demand approach) are used to modify a set of prior beliefs (the engineering approach), transforming them into a posterior distribution that describes appliance usage patterns and reflects the evidence provided by both approaches. Coauthors are Joseph A. Herriges, Kenneth E. Train, and Robert J. Windle.

Suggested Citation

  • Herriges, Joseph A. & Caves, Douglas W. & Train, K. & Windle, R. J., 1987. "A Bayesian Approach to Combining Conditional Demand and Engineering Models of Electricity Usage," Staff General Research Papers Archive 10794, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:10794
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    Cited by:

    1. Perrels, A. & Nijkamp, P., 1987. "Energy demand in a long-term perspective : possible implications of time scheduling," Serie Research Memoranda 0060, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    2. Frondel, Manuel & Sommer, Stephan & Vance, Colin, 2019. "Heterogeneity in German Residential Electricity Consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 131(C), pages 370-379.
    3. Papineau, Maya & Yassin, Kareman & Newsham, Guy & Brice, Sarah, 2021. "Conditional demand analysis as a tool to evaluate energy policy options on the path to grid decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    4. Bartels, Robert & Fiebig, Denzil G., 1995. "Optimal design in end-use metering experiments," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 39(3), pages 305-309.
    5. Hannah Goozee, 2017. "Energy, poverty and development: a primer for the Sustainable Development Goals," Working Papers 156, International Policy Centre for Inclusive Growth.
    6. Marcin Zygmunt & Dariusz Gawin, 2021. "Application of Artificial Neural Networks in the Urban Building Energy Modelling of Polish Residential Building Stock," Energies, MDPI, vol. 14(24), pages 1-15, December.
    7. Bashir, Kamaleldin Ali, 1990. "Technical change in Iowa agricultural production: a conditional demand approach," ISU General Staff Papers 1990010108000017619, Iowa State University, Department of Economics.
    8. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
    9. Yu, Dongwei & Tan, Hongwei & Ruan, Yingjun, 2012. "An improved two-step floating catchment area method for supporting district building energy planning: A case study of Yongding County city, China," Applied Energy, Elsevier, vol. 95(C), pages 156-163.
    10. Farzan, Farbod & Jafari, Mohsen A. & Gong, Jie & Farzan, Farnaz & Stryker, Andrew, 2015. "A multi-scale adaptive model of residential energy demand," Applied Energy, Elsevier, vol. 150(C), pages 258-273.
    11. Hannah Goozee, 2017. "Energy, Poverty and Development: A Primer for the Sustainable Development Goals," Working Papers id:11933, eSocialSciences.
    12. 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.
    13. 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.
    14. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).

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