TY - GEN
T1 - Reinforcement Learning for Battery Management in Dairy Farming
AU - Ali, Nawazish
AU - Wahid, Abdul
AU - Shaw, Rachael
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 is a particularly energy-intensive part of the agriculture sector. Effective battery management is essential for renewable integration within the agriculture sector. However, controlling battery charging/discharging is a difficult task due to electricity demand variability, stochasticity of renewable generation, and energy price fluctuations. Despite the potential benefits of applying Artificial Intelligence (AI) to renewable energy in the context of dairy farming, there has been limited research in this area. This research is a priority for Ireland as it strives to meet its governmental goals in energy and sustainability. This research paper utilizes Q-learning to learn an effective policy for charging and discharging a battery within a dairy farm setting. The results demonstrate that the developed policy significantly reduces electricity costs compared to the established baseline algorithm. These findings highlight the effectiveness of reinforcement learning for battery management within the dairy farming sector.
AB - Dairy farming is a particularly energy-intensive part of the agriculture sector. Effective battery management is essential for renewable integration within the agriculture sector. However, controlling battery charging/discharging is a difficult task due to electricity demand variability, stochasticity of renewable generation, and energy price fluctuations. Despite the potential benefits of applying Artificial Intelligence (AI) to renewable energy in the context of dairy farming, there has been limited research in this area. This research is a priority for Ireland as it strives to meet its governmental goals in energy and sustainability. This research paper utilizes Q-learning to learn an effective policy for charging and discharging a battery within a dairy farm setting. The results demonstrate that the developed policy significantly reduces electricity costs compared to the established baseline algorithm. These findings highlight the effectiveness of reinforcement learning for battery management within the dairy farming sector.
KW - Battery management
KW - Dairy Farming
KW - Maximizing self Consumption
KW - Q-learning
KW - Reinforcement Learning
KW - Time of Use
UR - http://www.scopus.com/inward/record.url?scp=85184300569&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-50485-3_26
DO - 10.1007/978-3-031-50485-3_26
M3 - Conference contribution
AN - SCOPUS:85184300569
SN - 9783031504846
T3 - Communications in Computer and Information Science
SP - 246
EP - 253
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 -
