Reinforcement Learning for Battery Management in Dairy Farming

Nawazish Ali; Abdul Wahid; Rachael Shaw; Karl Mason

doi:10.1007/978-3-031-50485-3_26

Reinforcement Learning for Battery Management in Dairy Farming

Nawazish Ali
, Abdul Wahid
, Rachael Shaw
, Karl Mason

Department of Enterprise and Technology

National University of Ireland, Galway

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI 2023, Proceedings
Editors	Sławomir Nowaczyk, Przemysław Biecek, Neo Christopher Chung, Mauro Vallati, Paweł Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomáš Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczyslaw Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	246-253
Number of pages	8
ISBN (Print)	9783031504846
DOIs	https://doi.org/10.1007/978-3-031-50485-3_26
Publication status	Published - 2024
Event	International Workshops of the 26th European Conference on Artificial Intelligence, ECAI 2023 - Kraków, Poland Duration: 30 Sep 2023 → 4 Oct 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1948 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	International Workshops of the 26th European Conference on Artificial Intelligence, ECAI 2023
Country/Territory	Poland
City	Kraków
Period	30/09/23 → 4/10/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Battery management
Dairy Farming
Maximizing self Consumption
Q-learning
Reinforcement Learning
Time of Use

Access to Document

10.1007/978-3-031-50485-3_26

Cite this

Ali, N., Wahid, A., Shaw, R., & Mason, K. (2024). Reinforcement Learning for Battery Management in Dairy Farming. In S. Nowaczyk, P. Biecek, N. C. Chung, M. Vallati, P. Skruch, J. Jaworek-Korjakowska, S. Parkinson, A. Nikitas, M. Atzmüller, T. Kliegr, U. Schmid, S. Bobek, N. Lavrac, M. Peeters, R. van Dierendonck, S. Robben, E. Mercier-Laurent, G. Kayakutlu, M. L. Owoc, K. Mason, A. Wahid, P. Bruno, F. Calimeri, F. Cauteruccio, G. Terracina, D. Wolter, J. L. Leidner, M. Kohlhase, ... V. Dimitrova (Eds.), Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI 2023, Proceedings (pp. 246-253). (Communications in Computer and Information Science; Vol. 1948 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-50485-3_26

Ali, Nawazish ; Wahid, Abdul ; Shaw, Rachael et al. / Reinforcement Learning for Battery Management in Dairy Farming. Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI 2023, Proceedings. editor / Sławomir Nowaczyk ; Przemysław Biecek ; Neo Christopher Chung ; Mauro Vallati ; Paweł Skruch ; Joanna Jaworek-Korjakowska ; Simon Parkinson ; Alexandros Nikitas ; Martin Atzmüller ; Tomáš Kliegr ; Ute Schmid ; Szymon Bobek ; Nada Lavrac ; Marieke Peeters ; Roland van Dierendonck ; Saskia Robben ; Eunika Mercier-Laurent ; Gülgün Kayakutlu ; Mieczyslaw Lech Owoc ; Karl Mason ; Abdul Wahid ; Pierangela Bruno ; Francesco Calimeri ; Francesco Cauteruccio ; Giorgio Terracina ; Diedrich Wolter ; Jochen L. Leidner ; Michael Kohlhase ; Vania Dimitrova. Springer Science and Business Media Deutschland GmbH, 2024. pp. 246-253 (Communications in Computer and Information Science).

@inproceedings{82b02861d0e7444cb1390ccc8fed1e95,

title = "Reinforcement Learning for Battery Management in Dairy Farming",

abstract = "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.",

keywords = "Battery management, Dairy Farming, Maximizing self Consumption, Q-learning, Reinforcement Learning, Time of Use",

author = "Nawazish Ali and Abdul Wahid and Rachael Shaw and Karl Mason",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; International Workshops of the 26th European Conference on Artificial Intelligence, ECAI 2023 ; Conference date: 30-09-2023 Through 04-10-2023",

year = "2024",

doi = "10.1007/978-3-031-50485-3\_26",

language = "English",

isbn = "9783031504846",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "246--253",

editor = "S{\l}awomir Nowaczyk and Przemys{\l}aw Biecek and Chung, \{Neo Christopher\} and Mauro Vallati and Pawe{\l} Skruch and Joanna Jaworek-Korjakowska and Simon Parkinson and Alexandros Nikitas and Martin Atzm{\"u}ller and Tom{\'a}{\v s} Kliegr and Ute Schmid and Szymon Bobek and Nada Lavrac and Marieke Peeters and \{van Dierendonck\}, Roland and Saskia Robben and Eunika Mercier-Laurent and G{\"u}lg{\"u}n Kayakutlu and Owoc, \{Mieczyslaw Lech\} and Karl Mason and Abdul Wahid and Pierangela Bruno and Francesco Calimeri and Francesco Cauteruccio and Giorgio Terracina and Diedrich Wolter and Leidner, \{Jochen L.\} and Michael Kohlhase and Vania Dimitrova",

booktitle = "Artificial Intelligence. ECAI 2023 International Workshops - XAI\textasciicircum{}3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI 2023, Proceedings",

}

Ali, N, Wahid, A, Shaw, R & Mason, K 2024, Reinforcement Learning for Battery Management in Dairy Farming. in S Nowaczyk, P Biecek, NC Chung, M Vallati, P Skruch, J Jaworek-Korjakowska, S Parkinson, A Nikitas, M Atzmüller, T Kliegr, U Schmid, S Bobek, N Lavrac, M Peeters, R van Dierendonck, S Robben, E Mercier-Laurent, G Kayakutlu, ML Owoc, K Mason, A Wahid, P Bruno, F Calimeri, F Cauteruccio, G Terracina, D Wolter, JL Leidner, M Kohlhase & V Dimitrova (eds), Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI 2023, Proceedings. Communications in Computer and Information Science, vol. 1948 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 246-253, International Workshops of the 26th European Conference on Artificial Intelligence, ECAI 2023, Kraków, Poland, 30/09/23. https://doi.org/10.1007/978-3-031-50485-3_26

Reinforcement Learning for Battery Management in Dairy Farming. / Ali, Nawazish; Wahid, Abdul; Shaw, Rachael et al.
Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI 2023, Proceedings. ed. / Sławomir Nowaczyk; Przemysław Biecek; Neo Christopher Chung; Mauro Vallati; Paweł Skruch; Joanna Jaworek-Korjakowska; Simon Parkinson; Alexandros Nikitas; Martin Atzmüller; Tomáš Kliegr; Ute Schmid; Szymon Bobek; Nada Lavrac; Marieke Peeters; Roland van Dierendonck; Saskia Robben; Eunika Mercier-Laurent; Gülgün Kayakutlu; Mieczyslaw Lech Owoc; Karl Mason; Abdul Wahid; Pierangela Bruno; Francesco Calimeri; Francesco Cauteruccio; Giorgio Terracina; Diedrich Wolter; Jochen L. Leidner; Michael Kohlhase; Vania Dimitrova. Springer Science and Business Media Deutschland GmbH, 2024. p. 246-253 (Communications in Computer and Information Science; Vol. 1948 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 - https://www.scopus.com/pages/publications/85184300569

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 -

Ali N, Wahid A, Shaw R, Mason K. Reinforcement Learning for Battery Management in Dairy Farming. In Nowaczyk S, Biecek P, Chung NC, Vallati M, Skruch P, Jaworek-Korjakowska J, Parkinson S, Nikitas A, Atzmüller M, Kliegr T, Schmid U, Bobek S, Lavrac N, Peeters M, van Dierendonck R, Robben S, Mercier-Laurent E, Kayakutlu G, Owoc ML, Mason K, Wahid A, Bruno P, Calimeri F, Cauteruccio F, Terracina G, Wolter D, Leidner JL, Kohlhase M, Dimitrova V, editors, Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 246-253. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-50485-3_26