Application-oriented modelling of domestic energy demand

• Published in: International journal of electrical power & energy systems Vol. 61; pp. 656 - 664
• Main Authors: Gruber, J.K., Jahromizadeh, S., Prodanović, M., Rakočević, V.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-10-2014; Elsevier
• Subjects: Applied sciences; Demand side management; Domestic energy consumption; Electrical engineering. Electrical power engineering; Electrical power engineering; Energy demand model; Exact sciences and technology; Load profile generation; Operation. Load control. Reliability; Power networks and lines; Demand side management; Domestic energy consumption; Load profile generation; Energy demand model; Modular construction; Modular architecture; Probabilistic approach; Data acquisition; Clientele; Power system economics; Algorithm; Flexibility; Modeling; Energy supply; Domestic appliances; Pricing; Electric power consumption; System simulation; Energy demand; Power markets; Economic aspect; Tariffication; Energy management
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2014.04.008

•Probabilistic model for generation of residential energy consumption profiles.•Development of new services and applications based on synthetic energy demand data.•Simulation of effect of modified user behaviour or changed appliance configuration.•Quantitative evaluation of the benefits of aggregated demand optimisation.
Detailed residential energy consumption data can be used to offer advanced services and provide new business opportunities to all participants in the energy supply chain, including utilities, distributors and customers. The increasing interest in the residential consumption data is behind the roll-out of smart meters in large areas and led to intensified research efforts in new data acquisition technologies for the energy sector. This paper introduces a novel model for generation of residential energy consumption profiles based on the energy demand contribution of each household appliance and calculated by using a probabilistic approach. The model takes into consideration a wide range of household appliances and its modular structure provides a high degree of flexibility. Residential consumption data generated by the proposed model are suitable for development of new services and applications such as residential real-time pricing schemes or tools for energy demand prediction. To demonstrate the main features of the model, an individual household consumption was created and the effects of a possible change in the user behaviour and the appliance configuration presented. In order to show the flexibility offered in creation of the aggregated demand, the detailed simulation results of an energy demand management algorithm applied to an aggregated user group are used.