Faculty Publications
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Publications by NITK Faculty
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Item Developing a building performance score model for assessing the sustainability of buildings(Emerald Publishing, 2022) Hp, T.; C, R.; Deepak, D.Purpose: Construction industry is one of the leading causes of pollution generation in today's context. But the fact that the development of construction industry leads to the country's economic and social development cannot be unobserved. Hence, there is a need to develop a sustainable construction methodology, and while doing so, measures must be considered so as to not disturb the natural habitats. With the greater prominence shown toward the concept of green and sustainable construction developments, various tools have been developed in recent years in order to measure the performance of such sustainable and green buildings. In the Indian context, the assessment tools developed to measure the performance of the green building are found to be scanty in addressing various economic and social impacts. Design/methodology/approach: This study aims at developing a building performance score (BPS) model concerning the sustainability model built on the triple bottom priorities considering all the three vital components, viz. environmental, economic and social factors. In this study, the different phases involved in the complete life cycle of the project are recognized and then all the phases are assessed considering all the three major components mentioned in the BPS model. Findings: The outcome of this study specifies that various indicators, such as the topographical and climate change, health and safety of the construction workers, project management consultancy, risk management, security measures and solid waste management, form a chief source of a sustainable building, and these indicators are not being assessed in the existing assessment tools. Also, consideration of environmental, economic and social factors is also equally important in construction industry. Moreover, these indicators are also required to be assessed and included in the evaluation process while assessing the performance of the building. Originality/value: The BPS model developed in the study will assist to improve in assessing the building performance with respect to all indicators in the complete life cycle of the project. © 2020, Emerald Publishing Limited.Item Assessing the life cycle performance of green building projects: a building performance score (BPS) model approach(Taylor and Francis Ltd., 2023) Thanu, T.; C, C.; Deepak, D.Construction industry is one of the major sectors contributing to the economic development of any country. Also, it acts as a major source of pollution towards the environment, and the impact of this is very severe. To overcome this, the concept of sustainability in the construction sector has emerged. In this regard, vital importance is given to the concept of sustainability along with various rating tools to measure green building performance. In the Indian context, existing assessment tools provide major importance to environmental impact rather than economic and social impacts. To address this issue, a Building Performance Score (BPS) model is developed based on the triple bottom priorities of sustainability which consists of environmental, economical, and social concepts. This model includes various indicators that play a major role in the sustainability assessment at various stages in life cycle of building. Different weights were ascertained for these indicators by experts and were further evaluated by Analytical Hierarchy Process (AHP) to understand the importance of these indicators. Furthermore, the importance of BPS model is validated considering three certified green buildings. Additional indicators that form the major source of sustainability that are neglected in the existing assessment tools are also considered in the case studies. The BPS model developed is utilized in different case scenarios to evaluate the performance of buildings and the suggested BPS model is validated through the present study. © 2022 Informa UK Limited, trading as Taylor & Francis Group.Item Comparative Study of Energy Efficiency Criteria for IGBC and GRIHA Systems Using Simulation(Springer, 2023) Rakesh, P.; Harisankar, R.; Das, B.B.Energy efficiency criteria are important in Green Building Rating (GBR) systems, and the requirements vary depending on the GBR system. A comparison study is conducted to distinguish between two major GBR systems used in India: Indian Green Building Council Green Homes (IGBC GH) and Green Rating for Integrated Habitat Assessment (GRIHA). The energy simulation software eQuest was used to forecast annual energy demand for a case study multi-family residential building with various design scenarios. Various design combinations for the roof envelope, wall envelope, and HVAC efficiency were chosen, and the possibilities of meeting the energy efficiency criteria of the two GBR systems were discussed. It was discovered that an improvement made based on envelope condition, the efficiency of the HVAC system considered in this study, and assigning renewable energy gives higher energy performance under IGBC Green Homes, with the possibility of achieving full credit points, but the combinations discussed did not give the full credit points for GRIHA. GRIHA, in particular, requires a significant reduction in HVAC load to receive full credit. GRIHA requires more renewable energy allocation than IGBC GH because renewable energy for achieving credit points is a percentage of HVAC, lighting, and domestic hot water consumption, whereas IGBC GH is a percentage of common area lighting consumption. © 2022, The Institution of Engineers (India).Item Anomalous Electrical Power Consumption Detection in Household Appliances via Micro-Moment Classification(Institute of Electrical and Electronics Engineers Inc., 2025) Nayak, R.; Jaidhar, C.D.The detection of anomalous power consumption is critical for improving energy efficiency, particularly with the increasing demand in buildings. This study explores Convolutional Neural Network-based models by transforming 1-dimensional micro-moment labeled data into 2-dimensional matrices to capture both temporal and spatial consumption patterns. Three architectural variants are investigated: a conventional Deep Convolutional Neural Network (DCNN), a Depthwise Separable Convolutional Neural Network (DS-CNN), and a Depthwise Separable Residual Convolutional Neural Network (DSR-CNN). Unlike earlier studies, this work incorporates hyperparameter tuning, statistical validation, and cross-validation, resulting in the evaluation of over 450 model configurations. The results indicate that while the DCNN consistently achieves the highest accuracy, the DS-CNN achieves comparable performance with significantly reduced parameters and computational cost, making it suitable for real-time and resource-constrained environments. Model complexity analysis and statistical tests confirm the robustness of the findings. Finally, a systematic model selection strategy is presented, identifying the DS-CNN as the most balanced solution for effective and efficient anomaly detection in smart grid applications. © 2020 IEEE.