[1] Gouveia, J., Seixas, J., Luo, S., Bilo, N., & Valentim, A. (2015). Understanding electricity consumption patterns in households through data fusion of smart meters and door to door surveys [Presentation]. Eceee summer study proceedings (pp. 957–966). https://www.researchgate.net/profile/Joao-Gouveia-12/publication/277879669_Understanding_electricity_consumption_patterns_in_households_through_data_fusion_of_smart_meters_and_door_to_door_surveys/links/57534ce608ae02ac127b0265/Understanding-electricity-c.
[2] Chesser, M., Hanly, J., Cassells, D., & Apergis, N. (2019). Household energy consumption: A study of micro renewable energy systems in ireland. Economic and social review, 50(2), 265–280.
[3] Larsen, B. M., & Nesbakken, R. (2004). Household electricity end-use consumption: Results from econometric and engineering models. Energy economics, 26(2), 179–200. DOI:10.1016/j.eneco.2004.02.001
[4] Baker, K. J., & Rylatt, R. M. (2008). Improving the prediction of UK domestic energy-demand using annual consumption-data. Applied energy, 85(6), 475–482. DOI:10.1016/j.apenergy.2007.09.004
[5] Yohanis, Y. G., Mondol, J. D., Wright, A., & Norton, B. (2008). Real-life energy use in the UK: How occupancy and dwelling characteristics affect domestic electricity use. Energy and buildings, 40(6), 1053–1059. DOI:10.1016/j.enbuild.2007.09.001
[6] McLoughlin, F., Duffy, A., & Conlon, M. (2012). Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study. Energy and buildings, 48, 240–248. DOI:10.1016/j.enbuild.2012.01.037
[7] Khorsandi, A., & Cao, B. Y. (2016). Stochastic residential load management utilizing fuzzy-based simplex optimization. NAPS 2016 - 48th north american power symposium, proceedings (pp. 1–5). IEEE. DOI: 10.1109/NAPS.2016.7747835
[8] Askeland, M., Burandt, T., & Gabriel, S. A. (2023). A stochastic MPEC approach for grid tariff design with demand-side flexibility. Energy systems, 14(3), 707–729. DOI:10.1007/s12667-020-00407-7
[9] Nezhad, A. E., Moeini-Aghtaie, M., & Ostovar, S. (2021). Demand response application for residential prosumers using stochastic optimization. 2021 11th smart grid conference, SGC 2021 (pp. 1–5). IEEE. DOI: 10.1109/SGC54087.2021.9664212
[10] Wang, Z., Paranjape, R., Chen, Z., & Zeng, K. (2020). Layered stochastic approach for residential demand response based on real-time pricing and incentive mechanism. IET generation, transmission and distribution, 14(3), 423–431. DOI:10.1049/iet-gtd.2019.1135
[11] Liu, J., Li, K., Yao, X., Wu, J., & Zhang, J. (2022). Research on modeling the optimization of power load combination for power sales company. Mobile information systems, 2022, 9566927. DOI:10.1155/2022/9566927
[12] Rodrigues, F., Cardeira, C., Calado, J. M. F., & Melicio, R. (2023). Short-term load forecasting of electricity demand for the residential sector based on modelling techniques: a systematic review. Energies, 16(10), 4098. DOI:10.3390/en16104098
[13] Osman, M., Ouf, M., Azar, E., & Dong, B. (2023). Stochastic bottom-up load profile generator for Canadian households’ electricity demand. Building and environment, 241, 110490. DOI:10.1016/j.buildenv.2023.110490
[14] Anvari, M., Proedrou, E., Schäfer, B., Beck, C., Kantz, H., & Timme, M. (2022). Data-driven load profiles and the dynamics of residential electricity consumption. Nature communications, 13(1), 4593. DOI:10.1038/s41467-022-31942-9
[15] Büttner, C., Amme, J., Endres, J., Malla, A., Schachler, B., & Cußmann, I. (2022). Open modeling of electricity and heat demand curves for all residential buildings in Germany. Energy informatics, 5(1), 1–21. DOI:10.1186/s42162-022-00201-y
[16] Shah, I., Jan, F., & Ali, S. (2022). Functional data approach for short-term electricity demand forecasting. Mathematical problems in engineering, 2022, 9566927. DOI:10.1155/2022/6709779
)