Abstract
Due to the impact of human lifestyle on building energy consumption, the development of occupants' behavior models is crucial for energy-saving purposes. In this regard, occupancy modeling is an effective approach to intend such a purpose. However, the literature reveals that existing occupancy models have limitations related to the representation of occupancy state duration and the integration of occupancy variability among individuals. Accordingly, this paper proposes an explicit differentiated duration probabilistic model to generate realistic daily occupancy profiles in residential buildings. The discrete-time Markov chain theory and the semi-parametric Cox proportional hazards model (Cox regression) are used to predict household occupancy profiles. The proposed model is able to capture occupancy states duration and integrate human behavior variability according to individuals' characteristics. Moreover, a parametric analysis is employed to investigate these characteristics' impact on the model performance and consequently, select the most significant input variables. A validation process is conducted by comparing the model performance with that of previous methods, presented in the literature. For this purpose, the k crossvalidation technique is utilized. Validation results demonstrate that the proposed approach is highly efficient in generating realistic household occupancy profiles.
Highlights
The electric grid faces a transformation in terms of growth and development due to global warming, huge electricity demand, and the impending depletion of fossil energy resources
The influence of socio-demographic characteristics on model performance has been analyzed through a parametric method
0.004:00 07:00 10:00 13:00Tim1e6o:0f0day19:00 22:00 01:00 04:00 (b) Results according to the age range to reduce the complexity of the model without affecting its accuracy in predicting occupancy profiles
Summary
The electric grid faces a transformation in terms of growth and development due to global warming, huge electricity demand, and the impending depletion of fossil energy resources. The building sector accounts for more than 30% of total world energy consumption that is expected to increase by an average of 1.5% per year between 2012-2040 [2]. This sector receives significant attention due to its energy-saving potentials, which can be up to 30% [3]. Energy consumption in buildings is influenced by different factors, including climate, building envelope, building services and energy systems, indoor environment quality, building operation and maintenance, and occupants’ behavior [4]. It can be described by occupants presence, and their interaction with appliances, control systems (e.g., heating, ventilation, and air conditioning), and building elements (e.g., doors and windows) [7]
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