AI-Enabled Intelligent Energy Optimisation for Smart Commercial Infrastructure Using IoT and Digital Twin Technologies
Avinash M. Pawar*1, Avinash M. Deshmukh2, Santosh R. Mitkari3, Sheetal S. Patil4, Nilofar Mulla5, Suresh Kumar Ray6
1Dr. Avinash M. Pawar, Department of Mechanical Engineering, Bharati Vidyapeeth College of Engineering for Women, Pune (Maharashtra), India.
2Dr. Avinash M. Deshmukh, Department of Basic Science and Engineering, SCTR’s Pune Institute of Computer Technology, Pune (Maharashtra), India.
3Dr. Santosh R. Mitkari, Department of Engineering Science and Allied Engineering, Bharati Vidyapeeth College of Engineering for Women, Pune (Maharashtra), India.
4Dr. Sheetal S. Patil, Department of Computer Science & Business Systems, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
5Dr. Nilofar Mulla, Department of Information Technology, Bharati Vidyapeeth College of Engineering for Women, Pune (Maharashtra), India.
6Dr. Suresh Kumar Ray, Bharati Vidyapeeth (Deemed to be University) College of Nursing, Sangli (Maharashtra), India.
Manuscript received on 20 September 2026 | First Revised Manuscript received on 01 October 2026 | Second Revised Manuscript received on 08 October 2026 | Manuscript Accepted on 15 October 2026 | Manuscript published on 30 October 2026 | PP: 1-5 | Volume-15 Issue-11, October 2026 | Retrieval Number: 100.1/ijitee.L131015121126 | DOI: 10.35940/ijitee.L1310.15121126
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Intelligent, sustainable management systems are needed to address rapid urbanisation and rising energy consumption in commercial facilities. Traditional Building Management Systems (BMS) are reactive, non-predictive, and non-automated, and they lack AI support for decision-making. This paper introduces EcoMind, an Internet of Things (IoT)- based intelligent energy optimisation framework for smart commercial infrastructures, supported by artificial intelligence. It is implemented in a cloud-native system that incorporates IoT telemetry simulation, predictive analytics, Digital Twin monitoring, sustainability assessment, and a telemetry-based conversational AI module. Anomaly detection, predictive maintenance, occupancy-aware intelligence, and rule-based optimisation mechanisms provide real-time analysis of HVAC, lighting, elevator, escalator, parking, and environmental sensor data. The definitions used for this study are as follows: occupancy utilisation: ratio of actual occupancy over maximum occupancy; total energy consumption: total energy consumed by HVAC, lighting, elevator, escalator and parking systems; anomaly deviation: dif erence between the current sensor reading and the historical normal behaviour; optimisation efficiency: ratio of reduction in energy consumption after applying optimisation strategies to the total energy consumption. The major innovation is using the Gemini API’s large language model to integrate live infrastructure telemetry data into a pipeline, enabling real-time reasoning without relying on static datasets. Experimental results show a decrease in total energy consumption of 18–22%, improved response to anomaly detection and improved operational decisionmaking. The results confirm that Digital Twin intelligence and Telemetry AI is a powerful complement that enhances scalability, adaptability, and sustainability in smart commercial infrastructure systems.
Keywords: Smart Infrastructure; Grounded AI; Digital Twin; IoT Analytics; Energy Optimization.
Scope of the Article: Mechanical Engineering