3 Search Results

This study evaluates the effect of complete nationwide lockdown in 2020 on residential electricity demand across 13 Indian cities and the role of digitalisation using a public smart meter dataset. We undertake a data-driven approach to explore the energy impacts of work-from-home norms across five dwelling typologies. Our methodology includes climate correction, dimensionality reduction and machine learning-based clustering using Gaussian Mixture Models of daily load curves. Results show that during the lockdown, maximum daily peak demand increased by 150-200% as compared to 2018 and 2019 levels for one room-units (RM1), one bedroom-units (BR1) and two bedroom-units (BR2) which are typicalmore » for low- and middle-income families. While the upper-middle- and higher-income dwelling units (i.e., three (3BR) and more-than-three bedroom-units (M3BR)) saw night-time demand rise by almost 44% in 2020, as compared to 2018 and 2019 levels. Our results also showed that new peak demand emerged for the lockdown period for RM1, BR1 and BR2 dwelling typologies. We found that the lack of supporting socioeconomic and climatic data can restrict a comprehensive analysis of demand shocks using similar public datasets, which informed policy implications for India's digitalisation. We further emphasised improving the data quality and reliability for effective data-centric policymaking.« less

Rapid urbanization pressure and poverty have created a push for affordable housing within the global south. The design of affordable housing can have consequences on the thermal (dis)comfort and behaviour of the occupants, hence requiring an occupant-centric approach to ensure sustainability. This paper investigates occupant behaviour within the urban poor households of Mumbai, India and its impact on their thermal comfort and energy use. This study is a first-of-its-kind attempt to explore the socio-demographic characteristics and energy-related behaviour of low-income occupants within Indian context. Three occupant archetypes, Indifferent Consumers; Considerate Savers; and Conscious Conventionals, were identified from the behavioural andmore » psychographic characteristics gathered through a transverse field survey. A two-step clustering approach was adopted for occupant segmentation that highlighted considerable diversity in occupants’ adaptation measures, energy knowledge, energy habits, and their pro-environmental behaviour within similar socio-economic group. Building energy simulation of the representative archetype behaviour estimated up to 37% variations for air-conditioned and up to 8% variation for fan-assisted naturally ventilated housing units during peak summer months. The results from this study establish the significance of occupant factors in shaping energy demand and thermal comfort within low-income housing and pave way for developing occupant-centric building design strategies to serve this marginalized population. The developed low-income occupant archetypes would be useful for architects and energy modelers to generate realistic energy use profiles and improve building performance simulation results.« less

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or themore » whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.« less