Evaluating the Impact of Subsidies on Solar PV Adoption Using Multi-Agent Systems and Machine Learning

Iias Faiud; Michael Schukat; Karl Mason

doi:10.1109/ICCCMLA63077.2024.10871760

Evaluating the Impact of Subsidies on Solar PV Adoption Using Multi-Agent Systems and Machine Learning

Computer Science

University of Galway

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

1 Citation (Scopus)

Abstract

This study analyzes the impact of subsidies on the adoption of solar photovoltaic (PV) systems in Irish dairy farms using a multi-agent systems model. The model examines the interactions between policymakers and dairy farms, integrating a linear regression model to forecast electricity prices. Our findings demonstrate that higher subsidies significantly boost PV adoption rates, with adoption increasing from 2 % at a 40 % subsidy to 20.9 % at a 7 0 % subsidy. These results provide valuable insights for policymakers, highlighting the effectiveness of subsidies in promoting renewable energy adoption. This work underscores the potential of subsidy policies to enhance reliance on renewable energy, thereby reducing CO2 emissions and supporting the transition to sustainable energy sources.

Original language	English
Title of host publication	ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	32-38
Number of pages	7
ISBN (Electronic)	9798331505790
DOIs	https://doi.org/10.1109/ICCCMLA63077.2024.10871760
Publication status	Published - 2024
Event	6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024 - Hamburg, Germany Duration: 19 Oct 2024 → 20 Oct 2024

Publication series

Name	ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications

Conference

Conference	6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024
Country/Territory	Germany
City	Hamburg
Period	19/10/24 → 20/10/24

UN SDGs

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

SDG 7 Affordable and Clean Energy
SDG 13 Climate Action
SDG 17 Partnerships for the Goals

Keywords

Linear regression
Machine Learning
Multi-agent systems
Photovoltaics
Renewable energy

Access to Document

10.1109/ICCCMLA63077.2024.10871760

Cite this

Faiud, I., Schukat, M., & Mason, K. (2024). Evaluating the Impact of Subsidies on Solar PV Adoption Using Multi-Agent Systems and Machine Learning. In ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications (pp. 32-38). (ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCCMLA63077.2024.10871760

Faiud, Iias ; Schukat, Michael ; Mason, Karl. / Evaluating the Impact of Subsidies on Solar PV Adoption Using Multi-Agent Systems and Machine Learning. ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 32-38 (ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications).

@inproceedings{c43cb93de7e3469bb6a45bd4a7258910,

title = "Evaluating the Impact of Subsidies on Solar PV Adoption Using Multi-Agent Systems and Machine Learning",

abstract = "This study analyzes the impact of subsidies on the adoption of solar photovoltaic (PV) systems in Irish dairy farms using a multi-agent systems model. The model examines the interactions between policymakers and dairy farms, integrating a linear regression model to forecast electricity prices. Our findings demonstrate that higher subsidies significantly boost PV adoption rates, with adoption increasing from 2 \% at a 40 \% subsidy to 20.9 \% at a 7 0 \% subsidy. These results provide valuable insights for policymakers, highlighting the effectiveness of subsidies in promoting renewable energy adoption. This work underscores the potential of subsidy policies to enhance reliance on renewable energy, thereby reducing CO2 emissions and supporting the transition to sustainable energy sources.",

keywords = "Linear regression, Machine Learning, Multi-agent systems, Photovoltaics, Renewable energy",

author = "Iias Faiud and Michael Schukat and Karl Mason",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024 ; Conference date: 19-10-2024 Through 20-10-2024",

year = "2024",

doi = "10.1109/ICCCMLA63077.2024.10871760",

language = "English",

series = "ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "32--38",

booktitle = "ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications",

}

Faiud, I, Schukat, M & Mason, K 2024, Evaluating the Impact of Subsidies on Solar PV Adoption Using Multi-Agent Systems and Machine Learning. in ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications. ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications, Institute of Electrical and Electronics Engineers Inc., pp. 32-38, 6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024, Hamburg, Germany, 19/10/24. https://doi.org/10.1109/ICCCMLA63077.2024.10871760

Evaluating the Impact of Subsidies on Solar PV Adoption Using Multi-Agent Systems and Machine Learning. / Faiud, Iias; Schukat, Michael ; Mason, Karl.
ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications. Institute of Electrical and Electronics Engineers Inc., 2024. p. 32-38 (ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications).

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

TY - GEN

T1 - Evaluating the Impact of Subsidies on Solar PV Adoption Using Multi-Agent Systems and Machine Learning

AU - Faiud, Iias

AU - Schukat, Michael

AU - Mason, Karl

PY - 2024

Y1 - 2024

N2 - This study analyzes the impact of subsidies on the adoption of solar photovoltaic (PV) systems in Irish dairy farms using a multi-agent systems model. The model examines the interactions between policymakers and dairy farms, integrating a linear regression model to forecast electricity prices. Our findings demonstrate that higher subsidies significantly boost PV adoption rates, with adoption increasing from 2 % at a 40 % subsidy to 20.9 % at a 7 0 % subsidy. These results provide valuable insights for policymakers, highlighting the effectiveness of subsidies in promoting renewable energy adoption. This work underscores the potential of subsidy policies to enhance reliance on renewable energy, thereby reducing CO2 emissions and supporting the transition to sustainable energy sources.

AB - This study analyzes the impact of subsidies on the adoption of solar photovoltaic (PV) systems in Irish dairy farms using a multi-agent systems model. The model examines the interactions between policymakers and dairy farms, integrating a linear regression model to forecast electricity prices. Our findings demonstrate that higher subsidies significantly boost PV adoption rates, with adoption increasing from 2 % at a 40 % subsidy to 20.9 % at a 7 0 % subsidy. These results provide valuable insights for policymakers, highlighting the effectiveness of subsidies in promoting renewable energy adoption. This work underscores the potential of subsidy policies to enhance reliance on renewable energy, thereby reducing CO2 emissions and supporting the transition to sustainable energy sources.

KW - Linear regression

KW - Machine Learning

KW - Multi-agent systems

KW - Photovoltaics

KW - Renewable energy

UR - https://www.scopus.com/pages/publications/85219517397

U2 - 10.1109/ICCCMLA63077.2024.10871760

DO - 10.1109/ICCCMLA63077.2024.10871760

M3 - Conference Publication

AN - SCOPUS:85219517397

T3 - ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications

SP - 32

EP - 38

BT - ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

Y2 - 19 October 2024 through 20 October 2024

ER -

Faiud I, Schukat M , Mason K. Evaluating the Impact of Subsidies on Solar PV Adoption Using Multi-Agent Systems and Machine Learning. In ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications. Institute of Electrical and Electronics Engineers Inc. 2024. p. 32-38. (ICCCMLA 2024 - 6th International Conference on Cybernetics, Cognition and Machine Learning Applications). doi: 10.1109/ICCCMLA63077.2024.10871760

Evaluating the Impact of Subsidies on Solar PV Adoption Using Multi-Agent Systems and Machine Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this