Shobhit Chaturvedi

Pandit Deendayal Petroleum University

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Co-Authors

E. Rajasekar

Indian Institute of Technology Roorkee

Naimish Bhatt

Pandit Deendayal Petroleum University

Ritu Tiwari

National Bureau of Plant Genetic Resources

A. K. Wadhwani

Atal Bihari Vajpayee Indian Institute of Information Technology and Management

Kratika Sharma

Indian Institute of Information Technology, Pune

Dhruvesh Patel

Pandit Deendayal Petroleum University

Kratika Sharma

Indian Institute of Information Technology, Pune

Kumkum Bhattacharya

Pandit Deendayal Petroleum University

Sukumar Natarajan

University of Bath

Debasis Sarkar

Pandit Deendayal Petroleum University

Cooperative Institutions

Pandit Deendayal Petroleum University

Indian Institute of Information Technology, Pune

Sardar Vallabhbhai National Institute of Technology Surat

Indian Institute of Technology Roorkee

University of Bath

Madhav Institute of Technology & Science

Rajiv Gandhi Technical University

Nirma University

Universiti Sains Malaysia

Motilal Nehru National Institute of Technology

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A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India

Energy Policy (2022)

Shobhit Chaturvedi E. Rajasekar Sukumar Natarajan Nick McCullen

energy demand

Mean absolute error

10.1016/j.enpol.2022.113097

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Citations (98)

A spatio-temporal assessment and prediction of Ahmedabad’s urban growth between 1990–2030

Journal of Geographical Sciences (2022)

Shobhit Chaturvedi Kunjan Shukla E. Rajasekar Naimish Bhatt

Urban sprawl

Built-up area

Multilayer perceptron

10.1007/s11442-022-2023-4

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Citations (13)

Evaluating the Challenges and Strategies for SDG.15.3: A Case Study of Goa State

Research Square (Research Square) (2024)

Harshit Dubey Naimish Bhatt Shobhit Chaturvedi Ravi Kant

Abstract With increased anthropogenic activity and commitments toward various international organisations and countries to mitigate its ill effects, finding a sustainable way out is very important. The Sustainable Development Goals (SDGs) thus become an important part of our collective efforts. The present study aims at assessing SDG 15: Life on land (SDG 15.3.1) for India’s state of Goa following the good practices guidelines of the United Nations to Combat Desertification (UNCCD) by employing the “Trends. Earth” plug-in in QGIS software. The assessment of the indicator is based on three sub-indicators: soil carbon, productivity, and land cover. The study yields a total land area improvement of 81.16% to the overall area of the state. The individual sub-indicator of productivity depicted an improvement of 85.14%, whereas 8.70% area was with stable productivity. Other sub-indicators of land cover showed a larger portion of land as stable, with 90.26% and a minor improvement of 5.20%. The soil organic carbon depicted an improvement of 0.54% with a maximum stable soil organic carbon of 94.91%. Several factors, such as better resource utilisation, improved governance practices, and a more sustainable approach to development, have affected the overall improvement of the indicators. Over the years, these improvements have allowed the state to grow as a sustainable tourist attraction for Indian nationals and foreign visitors.

Desertification

Land Cover

10.21203/rs.3.rs-4652666/v1

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Evaluating the impact of land use land cover changes on urban ecosystem services in Nashik, India: a RS-GIS based approach

Environmental Earth Sciences (2024)

Kratika Sharma Ritu Tiwari A. K. Wadhwani Shobhit Chaturvedi

Land Cover

Environmental engineering science

10.1007/s12665-024-11965-9

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A Comparative Assessment of Unsupervised and Supervised Methodologies for LANDSAT 8 Satellite Image Classification

Lecture notes in civil engineering (2024)

Kratika Sharma Ritu Tiwari Shobhit Chaturvedi A. K. Wadhwani

Satellite image

10.1007/978-981-99-3557-4_3

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Grey Wolf algorithmic framework for building energy optimization in India's Köppen-Geiger climatic zones

Advances in Building Energy Research (2023)

Shobhit Chaturvedi E. Rajasekar

Grey Wolf Optimization (GWO) is an emerging evolutionary metaheuristic technique capable of solving challenging engineering problems. Despite its growing popularity, GWO's suitability for building design problems remains unexplored. This paper presents a novel algorithmic framework using EnergyPlus, EPLauncher and Matlab to implement a single and bi-objective GWO for building energy optimization. The goal is to identify optimal wall and window type, orientation, air conditioner's operational profiles and cooling setpoints consistent with minimum annual and peak cooling energy demands for a residential apartment building in five Köppen-Geiger climate zones across India. In place of testing the entire parametric space involving 5,76,000 possibilities, GWO identifies the optimal solutions inside 1250 trials (∼99% run reduction). The single and bi-objective GWO produces (83-97)% and (75-95)% annual and peak cooling demand reductions than a typical construction and operation scenario in the five climate zones. The optimized solutions recommend low thermal transmittance-high capacitance wall sections, 10–15% window-to-wall ratios and double glazed windows with a low solar gain coefficient. Further, optimal air conditioner operational parameters (setpoint and duration) are identified. The presented algorithmic framework is highly robust and can integrate can incorporate upcoming metaheuristic algorithms to perform single and multiobjective building energy optimizations.

Solar gain

Building energy simulation

Cooling load

Set point

10.1080/17512549.2023.2184422

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Citations (1)

Comparative Evaluation of Genetic and Grey Wolf Algorithms for Building Energy Optimization

Lecture notes in electrical engineering (2024)

Shobhit Chaturvedi E. Rajasekar Kratika Sharma

10.1007/978-981-97-4654-5_49

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Landfill site selection in hilly terrains: An integrated RS-GIS approach with AHP and VIKOR

Waste Management Bulletin (2025)

Shobhit Chaturvedi Naimish Bhatt Vatsal Shah Keval H. Jodhani Dhruvesh Patel

Site selection

VIKOR method

10.1016/j.wmb.2025.01.010

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Multi-objective Building Design Optimization under Operational Uncertainties Using the NSGA II Algorithm

Buildings (2020)

Shobhit Chaturvedi E. Rajasekar Sukumar Natarajan

Operational uncertainties play a critical role in determining potential pathways to reduce the building energy footprint in the Global South. This paper presents the application of a non-dominated sorting genetic (NSGA II) algorithm for multi-objective building design optimization under operational uncertainties. A residential building situated in a mid-latitude steppe and desert region (Köppen climate classification: BSh) in the Global South has been selected for our investigation. The annual building energy consumption and the total number of cooling setpoint unmet hours (h) were assessed over 13,122 different energy efficiency measures. Six Pareto optimal solutions were identified by the NSGA II algorithm. Robustness of Pareto solutions was evaluated by comparing their performance sensitivity over 162 uncertain operational scenarios. The final selection for the most optimal energy efficiency measure was achieved by formulating a robust multi-criteria decision function by incorporating performance, user preference, and reliability criteria. Results from this robust approach were compared with those obtained using a deterministic approach. The most optimal energy efficiency measure resulted in 9.24% lower annual energy consumption and a 45% lower number of cooling setpoint unmet h as compared to the base case.

Set point

Robustness

Cooling load

10.3390/buildings10050088

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Citations (34)

Application of a probabilistic LHS-PAWN approach to assess building cooling energy demand uncertainties

Building Simulation (2021)

Shobhit Chaturvedi E. Rajasekar

Latin Hypercube Sampling

Bootstrapping (finance)

Categorical variable

Sample (material)

10.1007/s12273-021-0815-6

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Citations (18)