Citation: Murataj, J.; Gupta, R.; Nicol, F. Developing Indoor Temperature Profiles of Albanian Homes for Baseline Energy Models in Relation to Contextual Factors. Energies 2022, 15, 3668. https:// doi.org/10.3390/en15103668 Academic Editor: Boris Igor Palella Received: 13 April 2022 Accepted: 11 May 2022 Published: 17 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article Developing Indoor Temperature Profiles of Albanian Homes for Baseline Energy Models in Relation to Contextual Factors Jonida Murataj 1 , Rajat Gupta 2, * and Fergus Nicol 1 1 School of Architecture, Oxford Brookes University, Oxford OX3 0BP, UK; 15005185@brookes.ac.uk (J.M.); jfnicol@brookes.ac.uk (F.N.) 2 Low Carbon Building Group, Oxford Institute for Sustainable Development, Oxford Brookes University, Oxford OX3 0BP, UK * Correspondence: rgupta@brookes.ac.uk Abstract: Oversimplifying occupant behaviour using static and standard schedules has been identi- fied as a limitation of building energy simulation tools. This paper describes the use of hierarchical cluster analysis to establish the most typical indoor temperature profiles of Albanian dwellings based on monitored indoor temperatures in winter and summer, along with building and occupant surveys undertaken in 49 randomly selected dwellings in Tirana. Three statistically different profiles were developed for each summer and winter, indicating that homes are used in different ways, as well as revealing possible comfort requirements. Furthermore, statistical analysis was undertaken to determine the strength of the association between the clusters and contextual factors related to the building, household, and occupancy. A statistically significant association was found between the presence of children and the clusters in winter, suggesting that families with dependents use more energy. Building-related factors including building type, building age, and wall insulation were found to be statistically significantly associated with clusters in summer. These profiles could provide more accurate outcomes of energy consumption of Albanian homes and energy savings from retrofits. They could also facilitate the development of low-energy strategies and policies for specific households. Keywords: occupant behaviour; indoor temperature profiles; cluster analysis; energy modelling 1. Introduction Buildings’ energy simulations are one of the most common methods for estimating the energy demand of existing buildings, as well as energy savings from retrofitting projects [1]. However, there is often a significant discrepancy between the predicted and actual energy consumption of buildings [2–12], which is influenced by different factors and is largely caused by the complex interdependencies that occur between the fabric, services, controls, and occupant behaviour [13,14]. The physical design factors such as building features and the effect of external weather are the focus of many building simulation tools, while it has been challenging to incorporate the interaction between occupants and buildings, even though there has been significant research on occupant behaviours in buildings in recent years [11]. Only 5 out of 27 factors that were suggested in previous studies reviewed by Wei, Jones and de Wilde [15], i.e., room type, occupancy, indoor relative humidity, outdoor climate, and time of day, have been used to model space-heating behaviour in building performance simulations (BPS). Generally, occupant behaviour is oversimplified and predefined through static schedules in building performance simulations, ignoring its stochastic nature, dynamics, and diversity [16]. Improving assumptions about building operation has been a key challenge in recent research [1,17–19]. The choice of setpoints and operation schedules used by the building simulation modeler is found to be one of the key factors in the performance gap [20]. Using a sensitivity Energies 2022, 15, 3668. https://doi.org/10.3390/en15103668 https://www.mdpi.com/journal/energies