A Stochastic Model for Residential User Activity Simulation

Abstract

User activities is an important input to energy modelling, simulation and performance studies of residential buildings. However, it is often difficult to obtain detailed data on user activities and related energy consumption data. This paper presents a stochastic model based on Markov chain to simulate user activities of the households with one or more family members, and formalizes the simulation processes under different conditions. A data generator is implemented to create fine-grained activity sequences that require only a small sample of time-use survey data as a seed. This paper evaluates the data generator by comparing the generated synthetic data with real data, and comparing other related work. The results show the effectiveness of the proposed modelling approach and the efficiency of generating realistic residential user activities.

Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

“…Data-driven approaches to predict the occupants' presence and behaviour in open office spaces and to analyse indoor comfort and energy consumption are most common if large data sets are available [20]. The algorithms based on approaches based on Markov chain were used in most of the analysed studies for simulating occupant presence or activities [24,25,[38][39][40]46]. Some other models, rarely used, include the Monte Carlo [35], regression and time-series models [36], Dynamic Markov Time-Window Inference (DMTWI), Auto-Regressive Moving Average (ARMA) and Support Vector Regression (SVR) models [42], agent-based models (ABM) [47], Gaussian approach [46,48], and artificial neural networks (ANN) [29,30,49,50].…”

“…The algorithms based on approaches based on Markov chain were used in most of the analysed studies for simulating occupant presence or activities [24,25,[38][39][40]46]. Some other models, rarely used, include the Monte Carlo [35], regression and time-series models [36], Dynamic Markov Time-Window Inference (DMTWI), Auto-Regressive Moving Average (ARMA) and Support Vector Regression (SVR) models [42], agent-based models (ABM) [47], Gaussian approach [46,48], and artificial neural networks (ANN) [29,30,49,50]. New approaches like extreme learning machine (ELM) and its modifications [11][12][13][14][15], narrative-based modelling and multi-criteria analysis [20], have also been used recently.…”

Conditional Multivariate Elliptical Copulas to Model Residential Load Profiles From Smart Meter Data

“…Bottom-up approach implements a Markov chain model that simulates the dwellers' behavior inside the households and their interaction with home appliances [12]. Usually, these methods are based on social demographic data [13], or appliances characteristics and consumption duration [14]- [17].…”

The Impact of Thermo-Modernization and Predictive Regulation on Heat Energy Consumption and Emission of Pollutants into the Atmosphere - The Case of Prefabricated Buildings

“…This can be understood from the evidence that similar households in similar buildings can have significant differences in energy consumption and that a change in the household population can lead to significant changes in energy consumption. Thanks to the use of new technologies and services in the housing sector, it is possible to thoroughly examine the impact of dynamic changes in user activity on energy consumption [18][19][20]. To estimate the amount of heat consumed in buildings, analyses usually try to find empirical correlations between weather conditions and heat demand.…”