• Energy Research

Abstract The gap between the actual and intended energy use for a building is often attributed to stochastic behaviour of occupants. This study systematically investigates the relationship of occupancy with plug and lighting loads energy consumption for several spaces of an institutional building floor. A new parameter ‘Energy-use per person (K)’ is introduced to explain the stochastic relationship between Energy and Occupancy. A model for K is developed as a function of occupancy using ‘Multiple non-linear regression (MNLR)’ and ‘Deep neural network (DNN)’ based algorithms. DNN algorithm shows a better prediction of K with less Mean absolute percentage error (MAPE) of 9.67% and 2.37% compared to 10.34% and 3.15% of MNLR for plug and lighting loads respectively. The model developed is used to estimate possible energy savings during occupied hours with a rule-based energy-use behaviour. Possible plug load energy savings are 8.9%, 3.1% and 1.3% for the classroom, open office, and computer room respectively. Similarly, possible lighting load energy savings are 65.1%, 43.6% and 38.4% for the classroom, open office, and computer room respectively. The study outcome, a robust and iterative ‘K model’ development process can be used as a support tool in decision making for facility management.

As urbanization continues to accelerate globally, energy demand in cities is reaching unprecedented levels, contributing to greenhouse gas emissions. In response, the concept of net-zero energy building (NZEB) is becoming a sustainable solution for urban energy needs. NZEB aims to achieve a net-zero energy footprint by balancing the energy it consumes with the energy it produces, primarily from renewable energy (RE) sources. This comprehensive literature review-based study explores the role of RE synergies in the context of urban NZEBs, including discussions on definition and development of NZEBs, RE-synergies for achieving NZEBs, sustainable trends and clusters of NZEBs, climate change impacts on NZEBs, their performance evaluation, policy and regulatory frameworks, and challenges and possible solutions related to NZEBs. It has been identified that while customizing NZEB definitions to align with regional energy supply and demand is important, the same is highly dependent on building architectural and micro-climate features. The assessment of climate change effects and NZEB practices should involve evaluating building energy equilibrium, occupant comfort, and interactions with the energy grid. There are still some technical, policy, and socio-economic challenges that need more attention to provide comprehensive solutions for further enhancing the sustainable development/performance of NZEBs and achieving their goal.

Abstract Occupancy information is one of the crucial variables in modelling and predicting the energy use in buildings. However, the presence of occupants is often stochastic in nature. In the presented case of eight space typologies derived from three institutional building blocks, a substantial variation in the correlation between occupancy and energy consumption is found for different space types during the semester and semester breaks for various resolutions of day and time. Further, it has been identified that a weak correlation between occupants and energy use is due to the use of common plug and lighting loads such as office printers, projectors, lab instruments, and fluorescent lamps. However, in the spaces studied, such as offices and computer rooms, the control of plug and lighting loads can be at individual occupancy levels for avoiding energy wastages. To characterize occupancy and energy consumption patterns by different space types and occupant types, this study develops and presents an integrated, data-driven modelling framework and results for different space types (like classrooms, studios, computer rooms, office spaces and laboratories and time resolution (hourly to semester-long intervals) for the case study building. It is found that the Deep Neural Network (DNN) model exhibits a slightly better prediction accuracy than conventional machine learning/regression models such as gradient boosting, support-vector network, and feed-forward neural network. However, in terms of computation time, the gradient boosting model is found to be faster than the DNN model for comparable outcomes. The developed integrated model will better equip the building facility management for an occupant-oriented control of building systems for energy efficiency.

Occupancy schedules and density can have a substantial influence on building plug, lighting, and air conditioning energy usage. In recent years, the study related to occupancy and its impact on building energy consumption has gained momentum and is also promoted by ASHRAE as it has created a multi-disciplinary group to encourage a comprehensive study of occupant behaviour in buildings. Past studies suggest that building systems do not consume the same energy and provide similar Indoor Environmental Quality (IEQ) to their designed specifications due to inaccurate assumptions of occupants and their behaviour. Supplying ASHRAE 62.1 specified minimum required ventilation based on accurate occupancy may lead to significant air-conditioning energy savings. However, the same strategy is not suitable in the current time since minimum required ventilation may not be sufficient to mitigate the SARS-CoV-2 virus spread in confined spaces. High-temperature cooling augmented with elevated air movement across an acceptable range of velocity can maintain the health and comfort of occupants by providing higher ventilation and without an energy penalty. The analysis of the literature highlights strengths, weaknesses, and key observations about the existing occupancy monitoring and occupancy-based building system control methods to help in the direction of future occupancy-based research.

Abstract Variable Air Volume (VAV) system serving multiple zones often shows energy wastage issues as it is not able to maintain ventilation requirements efficiently at part-load due to inaccurate assumptions of occupancy and inherent inability to detect and use actual occupancy in control. In this study, the operational data of a typical VAV system has been analysed to study the implications of VAV system on energy efficiency and Indoor Air Quality (IAQ), when controlled using occupancy. Three occupancy-based overlapping operational strategies are proposed: 1. Supply air of the zone is optimized to meet the minimum ventilation requirement and to maintain the zone temperature below 24 °C for both occupied and unoccupied zones; 2. In accordance with the 1st strategy, the supply air of the unoccupied zone, if unoccupied for more than 60 min but less than a day, is further minimized to maintain the zone temperature below 28 °C; and 3. In accordance with the 2nd strategy, no ventilation air is supplied to zones that are unoccupied for the entire day. Based on the outcome of this study, the proposed occupancy-based operational strategies show energy saving potential in the range of 23–34%, 19–38%, 21–31% and 24–34% for classroom, computer room, open office, and closed office zones respectively. The primary contribution of this study is the occupancy-based zone level VAV optimization process and its exploration of possible decision-making tools to save energy.