load profile, household electricity consumption, load modeling, prosumer.


The article analyzes various approaches to the modeling of daily electricity load schedules and proposes a methodology for improving the "bottom-up" behavioral model of the load of household consumers. The method is based on averaging generated random load schedules for a given type of household on a particular day of the year. Averaging is carried out by season and length of daylight. Next, working days and weekends are distinguished in each interval. Despite some differences, this work does not separate holidays, Saturdays, and Sundays to avoid unnecessary details. The selected parameters for dividing the averaging intervals allow you to obtain a relatively compact set of model data and, at the same time, preserve the features of the load on different days of the year and hours of the day. Household load profiles with varying steps of day distribution and the main difference of such distribution are considered. Models that more accurately convey the sporadic nature of prosumer consumption compared to typical load schedules are built for two types of households. A detailed analysis of the averaged load graphs was carried out in comparison with the typical graph and the generated load graphs. Averaged load schedules are suitable for modeling the operating modes and control algorithms of the prosumer energy generation and storage system.


Siano, P. Demand response and smart grids—A survey / Renewable and sustainable energy reviews 30, 2014, p. 461-478.

Miller, W.; Senadeera, M. Social transition from energy consumers to prosumers: Rethinking the purpose and functionality of eco-feedback technologies. Sustain. Cities Soc. 2017, 35, 615–625.

Espe, E.; Potdar, V.; Chang, E. Prosumer communities and relationships in smart grids: A literature review, evolution and future directions. Energies 2018, 11, 2528.

Івахнов А. В., Кулапін О. В., Данильченко Д. О., Федорчук С. О., Гриценко В. В. Дослідження перспектив застосування соціо-демографічних даних для аналізу потенціалу керування попитом. Вісник НТУ "ХПІ" – Харків : НТУ "ХПІ", 2022. – № 1 (4). – С. 11-16.

Wilkerson, J. T., Cullenward, D., Davidian, D., Weyant, J. P. End use technology choice in the National Energy Modeling System (NEMS): An analysis of the residential and commercial building sectors. / Energy Economics, 40, 2013, p.773-784.

Energy Information Administration (EIA), 2010c. Model documentation report: residential sector demand module of the National Energy Modeling System. Report No DOE/EIA-M067(2010)/

Кулапін О. В., Махотіло К. В. Моделювання смарт-мережі споживачів-просьюмерів з фото-електричними системами. Вісник НТУ "ХПІ" – Харків : НТУ "ХПІ", 2019.– № 14 (1339).– С. 61-66.

Huber M, Dimkova D, Hamacher T. Integration of wind and solar power in Europe: assessment of flexibility requirements. Energy 2014;69:236–46.

R. Pal, C. Chelmis, M. Frincu and V. Prasanna, "MATCH for the prosumer smart grid the algorithmics of real-time power balance", IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 12, pp. 3532-3546, Dec. 2016.

Chuan, L., & Ukil, A. (). Modeling and validation of electrical load profiling in residential buildings in Singapore. IEEE Transactions on Power Systems, 30(5), 2014, p. 2800-2809.

S. M. Souza, M. Gil, J. Sumaili, A. G. Madureira and J. A. P. Lopes, "Operation scheduling of prosumer with renewable energy sources and storage devices", Proc. 13th Int. Conf. Eur. Energy Market, pp. 1-5, Jun. 2016.

McLoughlin F., Duffy A., Conlon M. Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study / Energy and buildings, 48, 2012, p. 240-248.

Nagbe, K., Cugliari, J., & Jacques, J. Short-term electricity demand forecasting using a functional state space model. / Energies, 11(5), 2018, 1120,

A. Paudel, K. Chaudhari, C. Long and H. B. Gooi, "Peer-to-peer energy trading in a prosumer-based community microgrid: theoretic model", IEEE Trans. Ind. Electron., vol. 66, no. 8, pp. 6087-6097, Aug. 2019.

Dörner, D., Bauplan für eine Seele. 1. Aufl ed. 1999, Reinbek bei Hamburg: Rowohlt Verl. 831 S.

Pflugradt N., Muntwyler U. Synthesizing residential load profiles using behavior simulation / Energy Procedia, 122, 2017. p. 655–660.

Pflugradt, N., and B. Platzer. "Behavior based load profile generator for domestic hot water and electricity use." 12th International Conference on Energy Storage (Innostock), Lleida, Spain. 2012.