Analysis and modeling of active occupancy of the residential sector in Spain: An indicator of residential electricity consumption

Stochastic model for lighting's electricity consumption in the residential sector. Impact of energy saving actions

“…As already indicated, the results from this stochastic model of daily active occupancy profiles of Spanish households were obtained with a 10-minute resolution, discriminating between the number of residents in the households, their location in the different regions of the country and the type of day, all of which were parameters required for the simulation [16,21]. These active A c c e p t e d M a n u s c r i p t occupancy profiles, determined by the model from the parameters initially entered, will finally be used in the calculus algorithm of the lighting consumption model proposed in this paper.…”

“…These profiles were synthetically generated by simulating a stochastic model, grounded in Markov Chain probability theory and Monte-Carlo techniques [23,24], included inside the general lighting consumption model proposed in this paper. This stochastic model was implemented and validated for Spain by Lopez et al and its bases are described in detail in previous publications [16,[19][20][21]. It is also based on data extracted from Time Use Surveys (TUS) [22], conducted in Spain of 19,295 people who were at least 10 years old and living in a total of 9,541 homes.…”

“…Nevertheless, Spain was not included in these studies and the different schedule distributions, lifestyles and routines of Spain versus its neighboring countries indicate the importance of this present analysis [16].…”

Simulating residential demand response: Improving socio-technical assumptions in activity-based models of energy demand

“…For example, some purely activity-based models do not simulate actual household energy demand (Aerts, Minnen, Glorieux, Wouters, & Descamps, 2014;López-Rodríguez, Santiago, Trillo-Montero, Torriti, & Moreno-Munoz, 2013;Wilke, Haldi, Scartezzini, & Robinson, 2013), while Paatero and Lund (2006) simulate appliance use based on deriving switch-on probabilities from monitored electricity consumption data, rather than activity information.…”

Bottom-up Markov Chain Monte Carlo approach for scenario based residential load modelling with publicly available data

“…Machine learning approaches are applied to model household load curves based on smart meter data measurements [5,6]. The residential load curve can also only be modelled at the peak times [7,8]. The modelling of household load over multiple decades requires an adjusted modelling approach, for instance based on how typiclose all cal households, behaviour and appliances change over scenarios [9].…”