Energy optimization in academic environments through IoT and machine learning
DOI:
https://doi.org/10.35381/i.p.v8i14.4882Keywords:
Energy optimization, Internet of things, machine learning, electrical consumption prediction, academic environments, (UNESCO Thesaurus).Abstract
ABSTRACT
The study aimed to optimize energy consumption in an academic environment by integrating IoT technologies and machine learning techniques. A monitoring system was designed and implemented in the Information Technology career room, using energy measurement modules and ESP32 nodes with environmental and presence sensors, the data was recorded on a real-time basis. Subsequently, a predictive model based on Random Forest was applied to analyse consumption patterns and contrast them with the effective occupation of the space. The model achieved adequate performance and allowed the identification of periods of unjustified consumption. Based on the simulated scenarios, a daily saving of close to 2.75 kWh was estimated, equivalent to approximately 60 kWh per month. In conclusion, the system proved to be a viable and scalable alternative, capable of being replicated in other classrooms, laboratories and similar academic spaces.
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Copyright (c) 2025 Elkin Alexander Morocho-Belduma, Blade Steven Masache-Carrera, Dixys Hernandez-Rojas, Bertha Mazon-Olivo
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