Please use this identifier to cite or link to this item: https://accedacris.ulpgc.es/handle/10553/143978
Title: Efficient Heating System Management Through IoT Smart Devices
Authors: de la Puente-Gil, Álvaro
González-Martínez, Alberto
Rosales Asensio, Enrique 
Diez-Suárez, Ana-María
Blanes Peiró, Jorge-Juan
UNESCO Clasification: 3313 Tecnología e ingeniería mecánicas
3322 Tecnología energética
Keywords: Energy consumption
Heating
Intelligent device management
IoT applications
Issue Date: 2025
Journal: Machines 
Abstract: A novel approach to managing domestic heating systems through IoT technologies is introduced in this paper. The system optimizes energy consumption by dynamically adapting to electricity and fuel price fluctuations while maintaining user comfort. Integrating smart devices significantly reduce energy costs and offer a favorable payback period, positioning the solution as both sustainable and economically viable. Efficient heating management is increasingly critical amid growing energy and environmental concerns. This strategy uses IoT devices to collect real-time data on prices, consumption, and user preferences. Based on this data, the system adjusts heating settings intelligently to balance comfort and cost savings. IoT connectivity manages continuous monitoring and dynamic optimization in response to changing conditions. This study includes a real-case comparison between a conventional central heating system and an IoT-managed electric radiator setup. By applying automation rules linked to energy pricing and user habits, the system enhances energy efficiency, especially in cold climates. The economic evaluation shows that using low-cost IoT devices yields meaningful savings and achieves equipment payback within approximately three years. The results demonstrate the system’s effectiveness, demonstrating that smart, adaptive heating solutions can cut energy expenses without sacrificing comfort, while offering environmental and financial benefits.
URI: https://accedacris.ulpgc.es/handle/10553/143978
ISSN: 2075-1702
DOI: 10.3390/machines13080643
Source: Machines [ISSN 2075-1702], v. 13 (Julio 2025)
Appears in Collections:Artículos
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