Machine learning-driven prediction of electrical consumption in higher education institutions: a comparative analysis incorporating occupancy dynamics at Camarines Sur Polytechnic Colleges /
Rosalie I. Gutierrez.
- 133 leaves.
Master's Thesis (ME) - Engineering.
Includes bibliographic references.
Higher education institutions (HEIs) consume substantial energy, often comparable to small cities, due to operations such as lighting, HVAC systems, and electronic equipment. In the Philippines, energy costs are a major burden for public institutions, making efficiency essential for fiscal sustainability and compliance with the Energy Efficiency and Conservation Act of 2019. Camarines Sur Polytechnic Colleges (CSPC), a state-run institution in the Bicol Region, exemplifies these challenges, with consumption patterns shaped by student and employee populations, seasonal variations, and external shocks such as the COVID-19 pandemic. This study applies supervised machine learning models—Linear Regression, Random Forest, and Gradient Boosting—to predict CSPC’s monthly electricity consumption. Input features include student and employee population data, along with engineered variables such as pandemic indicators and seasonal flags, addressing gaps in traditional forecasting methods that often overlook occupancy dynamics. Analysis identified normalized year (0.2176), employee numbers (0.2036), and total occupancy (0.1632) as the primary drivers of CSPC’s energy use, accounting for 58.44% of the Random Forest model’s predictive power. Random Forest emerged as the most reliable model, achieving a testing of 0.6995. explaining nearly 70% of consumption variability with stable performance across data samples. Forecasts projected 289,495 kWh for late 2024 and 655,068 kWh for 2025, with peaks during enrollment months and lows in vacation periods. By integrating population dynamics and external factors, this study demonstrates the potential of machine learning for proactive energy management in HEIs. The findings support global efforts toward energy efficiency and align with the United Nations Sustainable Development Goal 7: Affordable and Clean Energy.