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Forecasting residential air conditioning loads

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  • Horowitz, Shira
  • Mauch, Brandon
  • Sowell, Fallaw

Abstract

A doubly censored Tobit model is used to forecast hourly air-conditioner usage for individual households. The model worked well over a wide range of temperatures, 9–38°C, making it possible to accurately forecast the electricity load for a variety of demand response applications including operational reserves for renewable energy integration. Individual models are simulated and summed to obtain aggregate forecasts and confidence intervals. The model allows for correlation between the individual shocks that occur in a region. This approach gives substantially more accurate results than the moving average method typically used for forecasting and measuring direct load control. Applying the model to data from three U.S. utilities produced mean square error values from 0.027 to 0.041 with average load values per customer ranging from 0.49 to 0.62kW.

Suggested Citation

  • Horowitz, Shira & Mauch, Brandon & Sowell, Fallaw, 2014. "Forecasting residential air conditioning loads," Applied Energy, Elsevier, vol. 132(C), pages 47-55.
    • Handle: RePEc:eee:appene:v:132:y:2014:i:c:p:47-55
    DOI: 10.1016/j.apenergy.2014.06.029
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    Cited by:

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    2. Yang Yuan & Neng Zhu & Haizhu Zhou & Hai Wang, 2021. "A New Model Predictive Control Method for Eliminating Hydraulic Oscillation and Dynamic Hydraulic Imbalance in a Complex Chilled Water System," Energies, MDPI, vol. 14(12), pages 1-23, June.
    3. Trotter, Ian Michael & Féres, José Gustavo & Bolkesjø, Torjus Folsland & de Hollanda, Lavínia Rocha, 2015. "Simulating Brazilian Electricity Demand Under Climate Change Scenarios," Working Papers in Applied Economics 208689, Universidade Federal de Vicosa, Departamento de Economia Rural.
    4. Coelho, Vitor N. & Weiss Cohen, Miri & Coelho, Igor M. & Liu, Nian & Guimarães, Frederico Gadelha, 2017. "Multi-agent systems applied for energy systems integration: State-of-the-art applications and trends in microgrids," Applied Energy, Elsevier, vol. 187(C), pages 820-832.
    5. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "Residential demand response scheme based on adaptive consumption level pricing," Energy, Elsevier, vol. 113(C), pages 301-308.
    6. Yin, Rongxin & Kara, Emre C. & Li, Yaping & DeForest, Nicholas & Wang, Ke & Yong, Taiyou & Stadler, Michael, 2016. "Quantifying flexibility of commercial and residential loads for demand response using setpoint changes," Applied Energy, Elsevier, vol. 177(C), pages 149-164.

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