(untitled) (Innovation & Development in Engineering and Applied Science, 2026)
The study focused on predicting energy consumption in wireless sensor networks, an integral part of the Internet of Things (IoT), using advanced predictive models. For the agricultural environment, a dataset obtained from the Institute of Electrical and Electronics Engineers (IEEE) repository was used, corresponding to a prototype energy harvesting system based on a thermoelectric generator (TEG). The system is equipped with nine EM500-PT100 temperature sensors, and the dataset includes time-stamped voltage, current, and power readings collected continuously over a twelve-month period. Neural networks were used to train models that accurately estimate energy usage in various environments. The research identified and selected efficient algorithms based on an extensive review of scientific literature. Recurrent neural networks (RNNs), long-short-term memory units (LSTMs), and gated recurrent units (GRUs) were measured based on their ability to predict energy consumption, resulting in satisfactory performance with a coefficient of determination (R2) above 92 %. The results demonstrated that the developed models can efficiently predict energy consumption, enabling optimized energy management and reduce operating costs in IoT applications. Furthermore, the research highlighted the importance of energy efficiency in environmental sustainability and extending the lifespan of IoT devices. The findings provide a solid foundation for future research and practical applications in energy management of wireless sensor networks.