Monitoring and analyzing a building’s energy consumption patterns are major components for an effective energy management system, which helps in understanding the building’s operational behavior under different conditions. Energy management teams of universities are responsible for monitoring, analyzing and maintaining energy consumption data of their buildings. The study was conducted to elucidate the status of energy consumption, and to identify and illustrate the potential weak areas found from the energy consumption gap analysis in the context of government HEIs. The gap in this study was addressed by providing a detailed assessment of energy consumption practices and insights in an intra-university context, primarily through the identification of key practices leading to energy-saving measures. The work presented in this paper relied on data obtained from energy audits from the complete set of buildings over several campuses. In the occurrence of disparity between the actual values and the theoretical values, a focused group discussion was conducted to determine the energy consumption practices. The researchers had identified and categorized lists of practices based on the literature and selected those most applicable for government-funded HEIs. The researchers identified the list of key practices anchoring three Energy Management models: ISO 5001, Energy Star and BRECSU Energy Management System. The study determined that addressing specific key practices leading to an effective energy management system helps develop clear and achievable goals and objectives that adopt the SMART concept in the implementation of energy management programs.

This study evaluated the instructional value and acceptability of Internet of Things (IoT) Smart Lights in Electrical Technology Education. A descriptive quantitative research design was used. Data were gathered from faculty members, students, and practitioners through a structured questionnaire. The study assessed the competencies developed by the respondents in design, construction, installation, and safety. It also examined the acceptability of the IoT Smart Lights based on functionality, consistency, perceived ease of use, perceived usefulness, and net benefits. The findings showed that the respondents developed a highly acceptable level of competency in design, construction, installation, and safety, with an overall mean of 4.43. The IoT Smart Lights were also rated highly acceptable for instructional use, with an overall mean of 4.91. Perceived ease of use obtained the highest rating, followed by perceived usefulness, net benefits, consistency, and functionality. These results indicate that the IoT Smart Lights can support hands-on learning, improve instructional delivery, strengthen safety awareness, and expose learners to automation-based electrical applications. However, further improvement may be made in safety reinforcement, circuit component identification, and voice-command responsiveness. The study concludes that IoT Smart Lights are suitable for classroom and laboratory integration in Electrical Technology Education. Keywords: Internet of Things, Iot Smart Lights, Electrical Technology Education, Instructional Innovation, Technology Acceptance Model, Smart Lighting

This study examined the development and acceptability of an intelligent automation system designed for waste segregation and monitoring at Cebu Technological University–Main Campus. The system integrated artificial intelligence (AI) and Internet of Things (IoT) technologies to improve waste segregation efficiency, support real-time monitoring, and enhance sustainable waste management practices. A descriptive research design was applied. Data were gathered through a structured questionnaire administered to faculty members, technical experts, and students with National Certificate II (NCII) qualifications in electronics-related fields. The evaluation of the developed system followed selected dimensions of Garvin’s Quality Model, including performance, features, reliability, and durability, together with the Technology Acceptance Model (TAM) indicators of perceived usefulness and perceived ease of use. Statistical analysis involved frequency distribution, percentage computation, weighted mean, and correlation analysis. The findings indicated high levels of positive perception regarding the system’s performance, operational reliability, functional features, and durability. Respondents also expressed strong acceptance of the system, particularly in terms of its ease of operation, efficiency in waste monitoring, and usefulness in improving waste management processes. Correlation analysis revealed significant relationships between perceived quality dimensions and system acceptability, suggesting that improvements in system performance and reliability increased user acceptance. The results demonstrate that intelligent automation technologies can support efficient waste segregation and monitoring while promoting environmental sustainability. The study recommends continued development of AI- and IoT-based waste management solutions, user training programs, and further system evaluation to enhance long-term operational effectiveness and adoption in institutional waste management programs. Keywords: Intelligent Automation, Artificial Intelligence, Internet Of Things, Waste Segregation, Sustainable Waste Management.