Machine learning-driven prediction of electrical consumption in higher education institutions: a comparative analysis incorporating occupancy dynamics at Camarines Sur Polytechnic Colleges / (Record no. 33665)

MARC details
000 -LEADER
fixed length control field 03060nam a2200301 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field CSPC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20261009091603.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 261009s2026 ph a b 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency CSPC
Language of cataloging eng
Transcribing agency CSPC
Description conventions rda
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) MTH ME G984m 2026
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Gutierrez, Rosalie I.,
Relator term author.
245 10 - TITLE STATEMENT
Title Machine learning-driven prediction of electrical consumption in higher education institutions: a comparative analysis incorporating occupancy dynamics at Camarines Sur Polytechnic Colleges /
Statement of responsibility, etc. Rosalie I. Gutierrez.
300 ## - PHYSICAL DESCRIPTION
Extent 133 leaves.
336 ## - CONTENT TYPE
Source rdacontent
Content type term text
337 ## - MEDIA TYPE
Source rdamedia
Media type term unmediated
338 ## - CARRIER TYPE
Source rdacarrier
Carrier type term volume
502 ## - DISSERTATION NOTE
Dissertation note Master's Thesis (ME) - Engineering.
Name of granting institution Camarines Sur Polytechnic Colleges, 2026.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographic references.
520 3# - SUMMARY, ETC.
Summary, etc. 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Higher education institutions.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Cerillo, Jose Eduardo B.,
Relator term adviser.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Pontillas, Virginia V.,
Relator term chair, panelist.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Pacer, Wenifredo L.,
Relator term panelist.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Dematera, Fernan D.,
Relator term panelist.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Koha item type Unpublished Materials
Classification part G984m
Item part ME
Call number prefix MTH
Call number suffix 2026
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Dewey Decimal Classification     Main Library Main Library Graduate School Library 10/09/2026   MTH ME G984m 2026 007568UM 10/09/2026 1-1 10/09/2026 Unpublished Materials