Sustainable Energy Management System for AIoT Solutions Using Multivariate and Multi-Step Battery State of Charge Forecasting

Farnaz Kashefinishabouri, Nizar Bouguila, Zachary Patterson

2024

Abstract

The convergence of Artificial Intelligence (AI) with Internet of Things (IoT) technologies, known as AIoT, is revolutionizing industries, including smart cities. However, this transformation introduces challenges in energy management. Addressing this issue while upholding responsible AI principles requires prioritizing the sustainability of AIoT solutions through using renewable energy sources. While renewable energy offers numerous advantages, its intermittent nature necessitates an effective power management system. Developing a power management system serving as a decision-making platform for AIoT-driven solutions is the goal of this study. This platform contains two critical components: accurate forecasts of battery “State of Charge” (SoC), and the implementation of appropriate control strategies, including energy consumption adjustments. This study focuses on accurate battery SoC forecasts, to this end, an experiment has been designed, and a data logging system has been developed to produce suitable data since publicly available datasets do not match the specific characteristics of this research. The SoC forecasting in this paper has been addressed as a multivariate and multi-step time series forecasting problem, benchmarking various models. Comprehensive evaluations on datasets with varying time intervals showed the Bi-GRU model outperforming others based on MAE and RMSE metrics.

Paper Citation

in Harvard Style

Kashefinishabouri F., Bouguila N. and Patterson Z. (2024). Sustainable Energy Management System for AIoT Solutions Using Multivariate and Multi-Step Battery State of Charge Forecasting. In Proceedings of the 13th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS; ISBN 978-989-758-702-3, SciTePress, pages 49-56. DOI: 10.5220/0012624800003714

in Bibtex Style

@conference{smartgreens24,
author={Farnaz Kashefinishabouri and Nizar Bouguila and Zachary Patterson},
title={Sustainable Energy Management System for AIoT Solutions Using Multivariate and Multi-Step Battery State of Charge Forecasting},
booktitle={Proceedings of the 13th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS},
year={2024},
pages={49-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012624800003714},
isbn={978-989-758-702-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS
TI - Sustainable Energy Management System for AIoT Solutions Using Multivariate and Multi-Step Battery State of Charge Forecasting
SN - 978-989-758-702-3
AU - Kashefinishabouri F.
AU - Bouguila N.
AU - Patterson Z.
PY - 2024
SP - 49
EP - 56
DO - 10.5220/0012624800003714
PB - SciTePress