Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{197397,
        author = {Gayatri Deepak Malani and Bhushan Rajendra Sawant and Koushal Ravindra Khichade and Harshad Rajesh Kharat},
        title = {Climate Based Energy Consumption Prediction System For University Campuses Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9354-9357},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197397},
        abstract = {This paper presents a climate-based energy consumption prediction system designed for university campuses using machine learning techniques. Energy usage in universities is highly dynamic due to varying environmental conditions and time-based patterns, making accurate forecasting a challenging task. The proposed system integrates historical energy consumption data with real-time weather parameters such as temperature, humidity, wind speed, rainfall, and solar radiation to improve prediction accuracy. Machine learning models including Random Forest Regressor and XGBoost Regressor are implemented to perform hourly and time-range energy predictions. The system also provides an interactive web-based dashboard for visualization and analysis of energy trends. Experimental results demonstrate high prediction accuracy, with the Random Forest model achieving an R² score of 0.9799 and XGBoost achieving 0.9609. The proposed system supports efficient energy management, reduces operational costs, and contributes to sustainable energy utilization in university environments.},
        keywords = {Climate data, Energy consumption prediction, Machine learning, Random Forest, Time-series forecasting, XGBoost},
        month = {April},
        }