TY - GEN
T1 - Modelling Electricity Consumption in Irish Dairy Farms Using Agent-Based Modelling
AU - Khaleghy, Hossein
AU - Wahid, Abdul
AU - Clifford, Eoghan
AU - Mason, Karl
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Dairy farming can be an energy intensive form of farming. Understanding the factors affecting electricity consumption on dairy farms is crucial for farm owners and energy providers. In order to accurately estimate electricity demands in dairy farms, it is necessary to develop a model. In this research paper, an agent-based model is proposed to model the electricity consumption of Irish dairy farms. The model takes into account various factors that affect the energy consumption of dairy farms, including herd size, number of milking machines, and time of year. The outputs are validated using existing state-of-the-art dairy farm modelling frameworks. The proposed agent-based model is fully explainable, which is an advantage over other Artificial Intelligence techniques, e.g. deep learning.
AB - Dairy farming can be an energy intensive form of farming. Understanding the factors affecting electricity consumption on dairy farms is crucial for farm owners and energy providers. In order to accurately estimate electricity demands in dairy farms, it is necessary to develop a model. In this research paper, an agent-based model is proposed to model the electricity consumption of Irish dairy farms. The model takes into account various factors that affect the energy consumption of dairy farms, including herd size, number of milking machines, and time of year. The outputs are validated using existing state-of-the-art dairy farm modelling frameworks. The proposed agent-based model is fully explainable, which is an advantage over other Artificial Intelligence techniques, e.g. deep learning.
UR - https://www.scopus.com/pages/publications/85184296889
U2 - 10.1007/978-3-031-50485-3_24
DO - 10.1007/978-3-031-50485-3_24
M3 - Conference Publication
AN - SCOPUS:85184296889
SN - 9783031504846
T3 - Communications in Computer and Information Science
SP - 230
EP - 237
BT - Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI 2023, Proceedings
A2 - Nowaczyk, Sławomir
A2 - Biecek, Przemysław
A2 - Chung, Neo Christopher
A2 - Vallati, Mauro
A2 - Skruch, Paweł
A2 - Jaworek-Korjakowska, Joanna
A2 - Parkinson, Simon
A2 - Nikitas, Alexandros
A2 - Atzmüller, Martin
A2 - Kliegr, Tomáš
A2 - Schmid, Ute
A2 - Bobek, Szymon
A2 - Lavrac, Nada
A2 - Peeters, Marieke
A2 - van Dierendonck, Roland
A2 - Robben, Saskia
A2 - Mercier-Laurent, Eunika
A2 - Kayakutlu, Gülgün
A2 - Owoc, Mieczyslaw Lech
A2 - Mason, Karl
A2 - Wahid, Abdul
A2 - Bruno, Pierangela
A2 - Calimeri, Francesco
A2 - Cauteruccio, Francesco
A2 - Terracina, Giorgio
A2 - Wolter, Diedrich
A2 - Leidner, Jochen L.
A2 - Kohlhase, Michael
A2 - Dimitrova, Vania
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Workshops of the 26th European Conference on Artificial Intelligence, ECAI 2023
Y2 - 30 September 2023 through 4 October 2023
ER -