Impact of Window-to-Wall Ratio on Heating Demand and Thermal Comfort When Considering a Variety of Occupant Behavior Profiles

Energy consumption and thermal comfort in residential buildings are highly influenced by occupant behavior, which exhibits a high level of day-to-day and dwelling-to-dwelling variance. Although occupant behavior stochastic models have been developed in the past, the analysis or selection of a building design parameter is typically based on simulations that use a single “average” occupant behavior schedule which does not account for all possible profiles. The objective of this study is to enhance the understanding of how window-to-wall ratio (WWR) of a residential unit affects heating demand and thermal comfort when considering occupant behavior diversity through a parametric analysis. To do so, a stochastic occupant behavior model generates a high number of possible profiles, which are then used as input in an energy simulation of the dwelling. As a result, one obtains probability distributions of energy consumption and comfort for different WWR values. The paper shows that the shape of the probability distributions is affected by WWR and dwelling orientation, and that the influence of different occupant behavior aspects on performance also varies with WWR. This work could help designers to better assess the impact of WWR for a large spectrum of possible occupant behavior profiles.

An Investigation of Occupant Energy-Saving Behavior in Vernacular Houses of Behramkale (Assos)

Despite its well-known potential to reduce energy use, the inquiry of whether vernacular architecture prompts its occupants to have energy-saving behavior has been neglected. This paper aims to investigate the influence of vernacular houses on the behavior of their occupants and other parameters affecting occupant behavior. Along with site observations, 117 surveys including multiple choice and open-ended questions were conducted with households living in vernacular houses and new houses in the historical settlement, Behramkale (Assos). A principal component analysis was conducted for the whole sample to determine whether there is a relationship between energy saving occupant behavior and energy use, household, and housing characteristics. Then further analyses were performed to explore the differences in descriptive properties of occupants. Household characteristics were found to be associated with occupant behavior. The females and married people tended to show more energy-saving behavior and sought to use their houses in more environmentally friendly ways. The older people were more likely to show no-cost energy-saving behavior. The households with high income and high-level education tended to invest in energy-efficient appliances but consumed more energy than other households. Besides the effects of household characteristics, historical heritage, and landscape values specific to the area influenced occupant behavior. Vernacular houses enabled the households to behave in a certain way and to continue the traditional daily habits related to sustainable, energy-saving behaviors.

Development of an Electrical Energy Consumption Model for Malaysian Households, Based on Techno-Socioeconomic Determinant Factors

Energy-saving strategies are required to address the increasing global CO2 and electrical energy consumption problems. Therefore, the determinant factors of electrical energy consumption consist of socio-demographic changes, occupant behavior, house and appliance characteristics, or so-called techno-socioeconomic factors, which all need to be assessed. Statistics models, such as the artificial neural network (ANN), can investigate the relationship among those factors. However, the previous ANN model only used limited factors and was conducted in the developed countries of subtropical regions with different determinant factors than those in the developing countries of tropical regions. Furthermore, the previous studies did not investigate the various impacts of techno-socioeconomic factors concerning the performance of the ANN model in estimating monthly electrical energy consumption. The current study develops a model with a more-in depth architecture by examining the effect of additional factors such as socio-demographics, house characteristics, occupant behavior, and appliance characteristics that have not been investigated concerning the model performance. Thus, a questionnaire survey was conducted from November 2017 to January 2018 with 214 university students. The best combination factors in explaining the monthly electrical energy consumption were developed from occupant behavior, with 81% of the variance and a mean absolute percentage error (MAPE) of 20.6%, which can be classified as a reasonably accurate model. The current study’s findings could be used as additional information for occupants or for companies who want to install photovoltaic or wind energy systems.

An adaptive control framework based on Reinforcement learning to balance energy, comfort and hygiene in heat pump water heating systems

A major challenge in the operation of water heating systems lies in the highly stochastic nature of occupant behavior in hot water use, which varies over different buildings and can change over the time. However, the current operational strategies of water heating systems are detached from occupant behavior, and follow a conservative and energy intensive approach to ensure the availability of hot water any time it is demanded. This paper proposes a Reinforcement learning-based control framework which can learn and adapt to the occupant behavior of each specific building and make a balance between energy use, occupant comfort and water hygiene. The proposed framework is compared to the conventional approach using the real-world measurements of hot water use behavior in a single family residential building. Although the monitoring campaign has been executed during home lockdown due to COVID-19, when the occupants exhibited a very different schedule and water use related behavior, the proposed framework has learned the occupant behavior over a relatively short period of 8 weeks and provided 24.5% energy use reduction over the conventional approach, while preserving occupant comfort and water hygiene.