Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-Based Approach

Abdul Wahid; Iias Faiud; Karl Mason

doi:10.1007/978-3-031-74633-8_22

Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-Based Approach

Abdul Wahid
, Iias Faiud
, Karl Mason

University of Galway

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

This article investigates the use of Deep Q-Networks (DQNs) to optimize decision-making for photovoltaic (PV) systems installations in the agriculture sector. The study develops a DQN framework to assist agricultural investors in making informed decisions considering factors such as installation budget, government incentives, energy requirements, system cost, and long-term benefits. By implementing a reward mechanism, the DQN learns to make data-driven decisions on PV integration. The analysis provides a comprehensive understanding of how DQNs can support investors in making decisions about PV installations in agriculture. This research has significant implications for promoting sustainable and efficient farming practices while also paving the way for future advancements in this field. By leveraging DQNs, agricultural investors can make optimized decisions that improve energy efficiency, reduce environmental impact, and enhance profitability. This study contributes to the advancement of PV integration in agriculture and encourages further innovation in this promising area.

Original language	English
Title of host publication	Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers
Editors	Rosa Meo, Fabrizio Silvestri
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	324-336
Number of pages	13
ISBN (Print)	9783031746321
DOIs	https://doi.org/10.1007/978-3-031-74633-8_22
Publication status	Published - 2025
Event	Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 - Turin, Italy Duration: 18 Sep 2023 → 22 Sep 2023

Publication series

Name	Communications in Computer and Information Science
Volume	2135 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023
Country/Territory	Italy
City	Turin
Period	18/09/23 → 22/09/23

UN SDGs

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

SDG 2 Zero Hunger
SDG 7 Affordable and Clean Energy
SDG 17 Partnerships for the Goals

Keywords

Agriculture
Decision-making
Deep Q-Networks
Renewable energy
Sustainability

Access to Document

10.1007/978-3-031-74633-8_22

Cite this

Wahid, A., Faiud, I., & Mason, K. (2025). Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-Based Approach. In R. Meo, & F. Silvestri (Eds.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers (pp. 324-336). (Communications in Computer and Information Science; Vol. 2135 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-74633-8_22

Wahid, Abdul ; Faiud, Iias ; Mason, Karl. / Integrating Renewable Energy in Agriculture : A Deep Reinforcement Learning-Based Approach. Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers. editor / Rosa Meo ; Fabrizio Silvestri. Springer Science and Business Media Deutschland GmbH, 2025. pp. 324-336 (Communications in Computer and Information Science).

@inproceedings{f522e92fe4574c9eb7eaf0c50bfa5281,

title = "Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-Based Approach",

abstract = "This article investigates the use of Deep Q-Networks (DQNs) to optimize decision-making for photovoltaic (PV) systems installations in the agriculture sector. The study develops a DQN framework to assist agricultural investors in making informed decisions considering factors such as installation budget, government incentives, energy requirements, system cost, and long-term benefits. By implementing a reward mechanism, the DQN learns to make data-driven decisions on PV integration. The analysis provides a comprehensive understanding of how DQNs can support investors in making decisions about PV installations in agriculture. This research has significant implications for promoting sustainable and efficient farming practices while also paving the way for future advancements in this field. By leveraging DQNs, agricultural investors can make optimized decisions that improve energy efficiency, reduce environmental impact, and enhance profitability. This study contributes to the advancement of PV integration in agriculture and encourages further innovation in this promising area.",

keywords = "Agriculture, Decision-making, Deep Q-Networks, Renewable energy, Sustainability",

author = "Abdul Wahid and Iias Faiud and Karl Mason",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 ; Conference date: 18-09-2023 Through 22-09-2023",

year = "2025",

doi = "10.1007/978-3-031-74633-8\_22",

language = "English",

isbn = "9783031746321",

series = "Communications in Computer and Information Science",

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

pages = "324--336",

editor = "Rosa Meo and Fabrizio Silvestri",

booktitle = "Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers",

}

Wahid, A, Faiud, I & Mason, K 2025, Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-Based Approach. in R Meo & F Silvestri (eds), Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers. Communications in Computer and Information Science, vol. 2135 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 324-336, Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023, Turin, Italy, 18/09/23. https://doi.org/10.1007/978-3-031-74633-8_22

Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-Based Approach. / Wahid, Abdul; Faiud, Iias; Mason, Karl.
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers. ed. / Rosa Meo; Fabrizio Silvestri. Springer Science and Business Media Deutschland GmbH, 2025. p. 324-336 (Communications in Computer and Information Science; Vol. 2135 CCIS).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

T1 - Integrating Renewable Energy in Agriculture

T2 - Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023

AU - Wahid, Abdul

AU - Faiud, Iias

AU - Mason, Karl

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - This article investigates the use of Deep Q-Networks (DQNs) to optimize decision-making for photovoltaic (PV) systems installations in the agriculture sector. The study develops a DQN framework to assist agricultural investors in making informed decisions considering factors such as installation budget, government incentives, energy requirements, system cost, and long-term benefits. By implementing a reward mechanism, the DQN learns to make data-driven decisions on PV integration. The analysis provides a comprehensive understanding of how DQNs can support investors in making decisions about PV installations in agriculture. This research has significant implications for promoting sustainable and efficient farming practices while also paving the way for future advancements in this field. By leveraging DQNs, agricultural investors can make optimized decisions that improve energy efficiency, reduce environmental impact, and enhance profitability. This study contributes to the advancement of PV integration in agriculture and encourages further innovation in this promising area.

AB - This article investigates the use of Deep Q-Networks (DQNs) to optimize decision-making for photovoltaic (PV) systems installations in the agriculture sector. The study develops a DQN framework to assist agricultural investors in making informed decisions considering factors such as installation budget, government incentives, energy requirements, system cost, and long-term benefits. By implementing a reward mechanism, the DQN learns to make data-driven decisions on PV integration. The analysis provides a comprehensive understanding of how DQNs can support investors in making decisions about PV installations in agriculture. This research has significant implications for promoting sustainable and efficient farming practices while also paving the way for future advancements in this field. By leveraging DQNs, agricultural investors can make optimized decisions that improve energy efficiency, reduce environmental impact, and enhance profitability. This study contributes to the advancement of PV integration in agriculture and encourages further innovation in this promising area.

KW - Agriculture

KW - Decision-making

KW - Deep Q-Networks

KW - Renewable energy

KW - Sustainability

UR - https://www.scopus.com/pages/publications/85216126147

U2 - 10.1007/978-3-031-74633-8_22

DO - 10.1007/978-3-031-74633-8_22

M3 - Conference Publication

AN - SCOPUS:85216126147

SN - 9783031746321

T3 - Communications in Computer and Information Science

SP - 324

EP - 336

BT - Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers

A2 - Meo, Rosa

A2 - Silvestri, Fabrizio

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 18 September 2023 through 22 September 2023

ER -

Wahid A, Faiud I, Mason K. Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-Based Approach. In Meo R, Silvestri F, editors, Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2025. p. 324-336. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-74633-8_22