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A platform for research: civil engineering, architecture and urbanism
Stochastic bottom-up load profile generator for Canadian households’ electricity demand
https://orcid.org/0000-0001-6840-3189
https://orcid.org/0000-0002-6827-269X
https://orcid.org/0000-0001-7273-4853
Abstract The residential energy demand timing and magnitude are highly impacted by occupants' behaviors and activities. However, acquiring a reliable data source for these activities is a vital challenge, especially on an urban scale. In this context, this paper presents a stochastic bottom-up model for generating electrical loads for residential buildings in Canada. The proposed model is developed to investigate the impact of different household characteristics, appliance stock, and energy behaviors on the timing and magnitude of non-HVAC energy loads at individual or multiple houses. The proposed tool includes four main modules for generating stochastic profiles of occupancy, lighting demand, appliance load, and domestic hot water demand (DWH). The model is calibrated using the Canadian time use survey (TUS), energy use statistics, and appliance ownership surveys. The model is scalable and can be extended to serve various applications by adding new modules and data sources in the future. This paper presents the model development methodology, generated high-resolution load profiles, and validation in comparison with actual measurements. Finally, the model is used for studying the impact of household characteristics on total energy use. Future work will include incorporating this model into a comprehensive agent-based model for designing and testing effective demand response programs.
Highlights A stochastic load profile generator is developed for residential buildings. Canadian TUS data is used to identify common patterns of activity schedules. Stochastic profiles are developed for occupancy, lighting, appliances, and DWH. The tool is demonstrated and validated using a case study. The impact of household characteristics on total energy use is studied.
Stochastic bottom-up load profile generator for Canadian households’ electricity demand
https://orcid.org/0000-0001-6840-3189
https://orcid.org/0000-0002-6827-269X
https://orcid.org/0000-0001-7273-4853
Abstract The residential energy demand timing and magnitude are highly impacted by occupants' behaviors and activities. However, acquiring a reliable data source for these activities is a vital challenge, especially on an urban scale. In this context, this paper presents a stochastic bottom-up model for generating electrical loads for residential buildings in Canada. The proposed model is developed to investigate the impact of different household characteristics, appliance stock, and energy behaviors on the timing and magnitude of non-HVAC energy loads at individual or multiple houses. The proposed tool includes four main modules for generating stochastic profiles of occupancy, lighting demand, appliance load, and domestic hot water demand (DWH). The model is calibrated using the Canadian time use survey (TUS), energy use statistics, and appliance ownership surveys. The model is scalable and can be extended to serve various applications by adding new modules and data sources in the future. This paper presents the model development methodology, generated high-resolution load profiles, and validation in comparison with actual measurements. Finally, the model is used for studying the impact of household characteristics on total energy use. Future work will include incorporating this model into a comprehensive agent-based model for designing and testing effective demand response programs.
Highlights A stochastic load profile generator is developed for residential buildings. Canadian TUS data is used to identify common patterns of activity schedules. Stochastic profiles are developed for occupancy, lighting, appliances, and DWH. The tool is demonstrated and validated using a case study. The impact of household characteristics on total energy use is studied.
Stochastic bottom-up load profile generator for Canadian households’ electricity demand
https://orcid.org/0000-0001-6840-3189
https://orcid.org/0000-0002-6827-269X
https://orcid.org/0000-0001-7273-4853
Abstract The residential energy demand timing and magnitude are highly impacted by occupants' behaviors and activities. However, acquiring a reliable data source for these activities is a vital challenge, especially on an urban scale. In this context, this paper presents a stochastic bottom-up model for generating electrical loads for residential buildings in Canada. The proposed model is developed to investigate the impact of different household characteristics, appliance stock, and energy behaviors on the timing and magnitude of non-HVAC energy loads at individual or multiple houses. The proposed tool includes four main modules for generating stochastic profiles of occupancy, lighting demand, appliance load, and domestic hot water demand (DWH). The model is calibrated using the Canadian time use survey (TUS), energy use statistics, and appliance ownership surveys. The model is scalable and can be extended to serve various applications by adding new modules and data sources in the future. This paper presents the model development methodology, generated high-resolution load profiles, and validation in comparison with actual measurements. Finally, the model is used for studying the impact of household characteristics on total energy use. Future work will include incorporating this model into a comprehensive agent-based model for designing and testing effective demand response programs.
Highlights A stochastic load profile generator is developed for residential buildings. Canadian TUS data is used to identify common patterns of activity schedules. Stochastic profiles are developed for occupancy, lighting, appliances, and DWH. The tool is demonstrated and validated using a case study. The impact of household characteristics on total energy use is studied.
Stochastic bottom-up load profile generator for Canadian households’ electricity demand
Osman, Mohamed (author) / Ouf, Mohamed (author) / Azar, Elie (author) / Dong, Bing (author)
Building and Environment ; 241
2023-06-02
Article (Journal)
Electronic Resource
English
Demand based price determination for electricity consumers in private households
| BASE | 2016