Optimizing Planning Strategies: A Machine Learning Forecasting Model for Energy Aggregators and Hydropower Producers

Sarah Di Grande, Mariaelena Berlotti, Salvatore Cavalieri, Roberto Gueli

2024

Abstract

The global push for higher renewable energy production is driven by concerns about climate change, pollution, and diminishing fossil fuel reserves. Governments, businesses, and communities worldwide prioritize cleaner energy sources like solar, wind, and hydroelectric, over traditional fuels. Technological advancements enhancing efficiency and cost-effectiveness have made renewables more competitive, catalyzing their growing dominance in the energy market. In this context, renewable energy forecasting models are fundamental for both operators of the energy market called energy aggregators, and prosumers for different reasons like planning, decision-making, energy sales optimization, and investment evaluation. Therefore, the present work aimed to develop a machine learning model designed for multi-step hydropower forecasting of plants integrated into Water Distribution Systems (WDSs). The Alcantara 1 Hydroelectric Plant, situated in Italy, was utilized as the case study. This plant generates electricity from the water flow utilized for municipal water supply, which is then sold to the medium voltage network, resulting in substantial remuneration. This innovative approach utilizes previously unused architectures like TCN and N-Beats, to provide multi-step hydropower forecasting for WDS-integrated plants, a special category of systems for which models have not yet been developed. Results indicate TCN as the most accurate model for addressing the proposed task.

Paper Citation

in Harvard Style

Di Grande S., Berlotti M., Cavalieri S. and Gueli R. (2024). Optimizing Planning Strategies: A Machine Learning Forecasting Model for Energy Aggregators and Hydropower Producers. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 490-501. DOI: 10.5220/0012626100003690

in Bibtex Style

@conference{iceis24,
author={Sarah Di Grande and Mariaelena Berlotti and Salvatore Cavalieri and Roberto Gueli},
title={Optimizing Planning Strategies: A Machine Learning Forecasting Model for Energy Aggregators and Hydropower Producers},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={490-501},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012626100003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Optimizing Planning Strategies: A Machine Learning Forecasting Model for Energy Aggregators and Hydropower Producers
SN - 978-989-758-692-7
AU - Di Grande S.
AU - Berlotti M.
AU - Cavalieri S.
AU - Gueli R.
PY - 2024
SP - 490
EP - 501
DO - 10.5220/0012626100003690
PB - SciTePress