Using neural networks to model long-term dependencies in occupancy behavior

Abstract

Models simulating household energy demand based on different occupant and household types and their behavioral patterns have received increasing attention over the last years due the need to better understand fundamental characteristics that shape the demand side. Most of the models described in the literature are based on Time Use Survey data and Markov chains. Due to the nature of the underlying data and the Markov property, it is not sufficiently possible to consider long-term dependencies over several days in occupant behavior. An accurate mapping of longterm dependencies in behavior is of increasing importance, e.g. for the determination of flexibility potentials of individual households urgently needed to compensate supplyside fluctuations of renewable based energy systems. The aim of this study is to bridge the gap between social practice theory, energy related activity modelling and novel machine learning approaches. The weaknesses of existing approaches are addressed by combining time use survey data with mobility data, which provide information about individual mobility behavior over periods of one week. In social practice theory, emphasis is placed on the sequencing and repetition of practices over time. This suggests that practices have a memory. Transformer models based on the attention mechanism and Long short-term memory (LSTM) based neural networks define the state of the art in the field of natural language processing (NLP) and are for the first time introduced in this paper for the generation of weekly activity profiles. In a first step an autoregressive model is presented, which generates synthetic weekly mobility schedules of individual occupants and thereby captures long-term dependencies in mobility behavior. In a second step, an imputation model enriches the weekly mobility schedules with detailed information about energy relevant at home activities. The weekly activity profiles build the basis for multiple use cases one of which is modelling consistent electricity, heat and mobility demand profiles of households. The approach developed provides the basis for making high-quality weekly activity data available to the general public without having to carry out complex application procedures.

Modeling of occupant behavior considering spatial variation: Geostatistical analysis and application based on American time use survey data

“…Tabak [15] developed a model called the User Simulation of Space Utilization that simulates space utilization in an office building by calculating the distances between the locations of different activities based on measured data. In addition to spatial utilization, the mobility and occupancy patterns of people can also be estimated based on dynamic spatial choices or preferences [16][17][18][19][20][21]. However, the variation of OBs over space has not been considered in these studies.…”

“…Each group was homogenized to avoid the influence of sociodemographic factors in the spatial variation as shown in Table 1. The conditions for segmentation were the type of day (i.e., weekdays and weekends) and employment status-commonly used parameters in previous studies [19,20,23,24,50]. Groups 1 and 4 represent women with fulltime jobs; Groups 2 and 5 represent women with part-time jobs; Groups 3 and 6 represent unemployed women.…”

Modelling of individual domestic occupancy and energy demand behaviours using existing datasets and probabilistic modelling methods

“…A different method by Kleinebrahm et al [23] using neural networks has been developed that also attempts to solve the same individual behaviour replication limitations of Markov Chains that the presented existing and new work by the Authors' also addresses.…”