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A high-resolution stochastic model of domestic activity patterns and electricity demand

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  • Widén, Joakim
  • Wäckelgård, Ewa

Abstract

Realistic time-resolved data on occupant behaviour, presence and energy use are important inputs to various types of simulations, including performance of small-scale energy systems and buildings' indoor climate, use of lighting and energy demand. This paper presents a modelling framework for stochastic generation of high-resolution series of such data. The model generates both synthetic activity sequences of individual household members, including occupancy states, and domestic electricity demand based on these patterns. The activity-generating model, based on non-homogeneous Markov chains that are tuned to an extensive empirical time-use data set, creates a realistic spread of activities over time, down to a 1-min resolution. A detailed validation against measurements shows that modelled power demand data for individual households as well as aggregate demand for an arbitrary number of households are highly realistic in terms of end-use composition, annual and diurnal variations, diversity between households, short time-scale fluctuations and load coincidence. An important aim with the model development has been to maintain a sound balance between complexity and output quality. Although the model yields a high-quality output, the proposed model structure is uncomplicated in comparison to other available domestic load models.

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  • Widén, Joakim & Wäckelgård, Ewa, 2010. "A high-resolution stochastic model of domestic activity patterns and electricity demand," Applied Energy, Elsevier, vol. 87(6), pages 1880-1892, June.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:6:p:1880-1892
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    4. Sandels, C. & Brodén, D. & Widén, J. & Nordström, L. & Andersson, E., 2016. "Modeling office building consumer load with a combined physical and behavioral approach: Simulation and validation," Applied Energy, Elsevier, vol. 162(C), pages 472-485.
    5. Liu, Luyao & Zhao, Yi & Chang, Dongliang & Xie, Jiyang & Ma, Zhanyu & Sun, Qie & Yin, Hongyi & Wennersten, Ronald, 2018. "Prediction of short-term PV power output and uncertainty analysis," Applied Energy, Elsevier, vol. 228(C), pages 700-711.
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    7. Muhammad Babar Rasheed & Nadeem Javaid & Ashfaq Ahmad & Mohsin Jamil & Zahoor Ali Khan & Umar Qasim & Nabil Alrajeh, 2016. "Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing," Energies, MDPI, vol. 9(8), pages 1-25, July.
    8. Sandels, C. & Widén, J. & Nordström, L., 2014. "Forecasting household consumer electricity load profiles with a combined physical and behavioral approach," Applied Energy, Elsevier, vol. 131(C), pages 267-278.
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    13. Deng, Nana & Wang, Bo & Qiu, Yueming & Liu, Jie & Shi, Han & Zhang, Bin & Wang, Zhaohua, 2022. "The discrepancies in the impacts of COVID-19 lockdowns on electricity consumption in China: Is the short-term pain worth it?," Energy Economics, Elsevier, vol. 114(C).
    14. Ghamlouch, Houda & Fouladirad, Mitra & Grall, Antoine, 2019. "The use of real option in condition-based maintenance scheduling for wind turbines with production and deterioration uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 614-623.
    15. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.

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