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Characterizing probability density distributions for household electricity load profiles from high-resolution electricity use data

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  • Munkhammar, Joakim
  • Rydén, Jesper
  • Widén, Joakim

Abstract

This paper presents a high-resolution bottom-up model of electricity use in an average household based on fit to probability distributions of a comprehensive high-resolution household electricity use data set for detached houses in Sweden. The distributions used in this paper are the Weibull distribution and the Log-Normal distribution. These fitted distributions are analyzed in terms of relative variation estimates of electricity use and standard deviation. It is concluded that the distributions have a reasonable overall goodness of fit both in terms of electricity use and standard deviation. A Kolmogorov–Smirnov test of goodness of fit is also provided. In addition to this, the model is extended to multiple households via convolution of individual electricity use profiles. With the use of the central limit theorem this is analytically extended to the general case of a large number of households. Finally a brief comparison with other models of probability distributions is made along with a discussion regarding the model and its applicability.

Suggested Citation

  • Munkhammar, Joakim & Rydén, Jesper & Widén, Joakim, 2014. "Characterizing probability density distributions for household electricity load profiles from high-resolution electricity use data," Applied Energy, Elsevier, vol. 135(C), pages 382-390.
    • Handle: RePEc:eee:appene:v:135:y:2014:i:c:p:382-390
    DOI: 10.1016/j.apenergy.2014.08.093
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    References listed on IDEAS

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    1. 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.
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    2. Joakim Munkhammar & Lars Mattsson & Jesper Rydén, 2017. "Polynomial probability distribution estimation using the method of moments," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
    3. Shepero, Mahmoud & van der Meer, Dennis & Munkhammar, Joakim & Widén, Joakim, 2018. "Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data," Applied Energy, Elsevier, vol. 218(C), pages 159-172.
    4. Alireza Vahabzadeh & Alibakhsh Kasaeian & Hasan Monsef & Alireza Aslani, 2020. "A Fuzzy-SOM Method for Fraud Detection in Power Distribution Networks with High Penetration of Roof-Top Grid-Connected PV," Energies, MDPI, vol. 13(5), pages 1-24, March.
    5. Fuentes-Cortés, Luis Fabián & Flores-Tlacuahuac, Antonio & Ponce-Ortega, José María, 2019. "Integrated utility pricing and design of water-energy rural off-grid systems," Energy, Elsevier, vol. 177(C), pages 511-529.
    6. Munkhammar, Joakim & Widén, Joakim & Rydén, Jesper, 2015. "On a probability distribution model combining household power consumption, electric vehicle home-charging and photovoltaic power production," Applied Energy, Elsevier, vol. 142(C), pages 135-143.
    7. Trespalacios, Alfredo & Cortés, Lina M. & Perote, Javier, 2020. "Uncertainty in electricity markets from a semi-nonparametric approach," Energy Policy, Elsevier, vol. 137(C).
    8. García-Villalobos, J. & Zamora, I. & Knezović, K. & Marinelli, M., 2016. "Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 180(C), pages 155-168.
    9. Moon, Sang-Keun & Kim, Jin-O, 2017. "Balanced charging strategies for electric vehicles on power systems," Applied Energy, Elsevier, vol. 189(C), pages 44-54.
    10. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    11. Ye, Chengjin & Ding, Yi & Song, Yonghua & Lin, Zhenzhi & Wang, Lei, 2018. "A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing," Applied Energy, Elsevier, vol. 232(C), pages 9-25.
    12. Luthander, Rasmus & Widén, Joakim & Munkhammar, Joakim & Lingfors, David, 2016. "Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment," Energy, Elsevier, vol. 112(C), pages 221-231.
    13. Yang, Ting & Ren, Minglun & Zhou, Kaile, 2018. "Identifying household electricity consumption patterns: A case study of Kunshan, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 861-868.

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