International Journal on Science and Technology

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AI-Based Connected Load Analysis and Energy Consumption Prediction for Methodist College of Engineering and Technology

Author(s) Dr. Jarapala Ramesh Babu, Yousuf Jaweed Hussain Chakali., D Niranjan Samudrala Umesh B K, Md Mustafa
Country India
Abstract Heterogeneous building functions, changing occupancy, and varied operation schedules determine power consumption, making energy management problematic in university campuses. In order to help Methodist College of Engineering and Technology with their linked load analysis and monthly energy consumption projection, this study presents a viable AI-based methodology. Using a monthly historical energy-consumption series from February 2025 to February 2026 and block-wise connected-load records for six campus blocks, the research integrates engineering load estimate with data-driven modelling. According to the campus's installed load of about 712.14 kW, the most significant demand centers are Blocks E, C, and A, as shown by the connected-load study. Monthly consumption peaked in April 2025 and dropped to a low in December 2025, indicating significant seasonal or operational fluctuation according to the time series study. For shorter-term predictions, we utilise linear regression as a comprehensible baseline, and we apply isolation forest to identify unusual months. The results demonstrate that the framework is capable of locating high-impact load zones, assisting with short-term demand planning, and exposing anomalous months that should be examined by managers. The study finds that structured AI-assisted analysis may improve energy planning at the campus level and provide the groundwork for smart-campus energy management enabled by the internet of things (IoT) in the future, even with a small dataset.
Keywords Energy Management, Campus Energy Forecasting, Machine Learning, Anomaly Detection
Field Engineering
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-04-15
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