Clustering of Household Occupancy Profiles for Archetype Building Models

Abstract

The continued penetration of renewable energy sources in electricity generation and the de-carbonization of the domestic space heating and hot water sectors is increasing the importance of demand side management (DSM). The development of end-use energy consumption models that can be easily integrated with electricity dispatch models is crucial for the assessment of the integration of supply and demand. The energy consumption of the domestic building stock is highly correlated with occupant behaviour, however the inclusion of occupant behaviour in energy models is challenging due to its highly variable nature. Nevertheless, in order to obtain reliable models of domestic energy consumption at high time resolution, the analysis of occupant behaviour patterns is fundamental. This paper aims to develop a new methodology to generate realistic occupancy patterns that can be representative of large numbers of households. This method is based on the clustering of household occupancy profiles using the UK 2000 Time Use Survey data as a case study. The occupancy profiles that result from this method can be used as input to residential building energy end-use models, thereby giving improved overall model performance.

Development of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypes

“…Occupancy schedules are developed to define archetypes in the "operation" subset. These correspond to the most common schedules of the building stock, which are obtained by applying the clustering technique to the data collected by national Time Use Surveys (TUSs), as in [20,28]…”

“…In order to identify the most common occupancy schedules in the building stock, the clustering methodology previously developed in [27,28] is applied on the available household daily occupancy profiles. The first step of the clustering methodology is the identification of the significant household states, the sequence of which determines the household daily occupancy profiles.…”

Occupant behavior: a “new” factor in energy performance of buildings. Methods for its detection in houses and in offices.

“…The authors [16] validated a stochastic model by means of French TUS with the aim to accurately predict residential building occupants time-dependent activities. British TUS were the inputs [17,18] to define high time-resolution occupancy profiles able to reproduce when occupants likely use home appliances, lighting, and heating.…”

Is Bicycle Sharing an Environmental Practice? Evidence from a Life Cycle Assessment Based on Behavioral Surveys

“…The existing questionnaire processing techniques include descriptive statistic [37][38][39], a simplest and most widely used method, regression analysis [40,41], correlation analysis and cluster analysis [42][43][44]. Since the purpose of the questionnaire in this paper is to observe the usage and attitude of different groups on bicycle sharing, the focus is to apply the replaced mileage of various alternative vehicles in subsequent calculation, this paper adopts the method of frequencies statistics and cross-analysis to deal with the collected questionnaire.…”