Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy

Diriba Gonfa Tolasa

doi:doi:10.11648/j.epes.20251402.11

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Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy

Diriba Gonfa Tolasa^*

Published in American Journal of Electrical Power and Energy Systems (Volume 14, Issue 2)

Received: 4 March 2025 Accepted: 2 April 2025 Published: 19 May 2025

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Abstract

The increasing demand for sustainable energy solutions has prompted significant interest in the integration of renewable energy sources into micro-grids. This paper presents a novel approach utilizing neural networks for the effective integration of hybrid photovoltaic (PV) and wind energy systems within micro-grids. The proposed framework addresses the inherent intermittency and variability associated with renewable energy sources, which can challenge grid stability and reliability. In recent years, there has been a growing recognition of the potential of neural networks to model complex non-linear relationships in energy generation and consumption. This study leverages advanced machine learning techniques to optimize the operation of micro-grids, enhancing the synergy between PV and wind energy systems. By employing a multi-layer perceptron (MLP) neural network, we are able to predict energy generation from both sources with high accuracy based on historical weather data and real-time operational parameters. The methodology involves a comprehensive analysis of the energy output from the hybrid system under varying climatic conditions. We utilize a combination of supervised learning algorithms to train the model on historical data, enabling it to forecast energy availability and optimize energy dispatch in real-time. Simulation results indicate a significant improvement in energy management efficiency, reducing reliance on conventional fossil fuel backup systems. Furthermore, the integration of energy storage systems is considered to mitigate fluctuations in power generation and ensure a stable energy supply. The results demonstrate that our neural network-driven approach can achieve a higher penetration of renewables in micro-grids, leading to enhanced economic viability and reduced greenhouse gas emissions. This study contributes to the field of sustainable energy by providing a robust framework for hybrid renewable energy integration, emphasizing the importance of advanced computational techniques. The findings underscore the potential of neural networks not only for predicting energy output but also for optimizing micro-grid operations, paving the way for more resilient and environmentally friendly energy systems. The implications of this research are significant for policymakers and energy planners seeking to implement effective strategies for renewable energy integration in micro-grid infrastructures. By fostering greater adoption of hybrid systems, we can move closer to realizing a sustainable energy future.

Published in	American Journal of Electrical Power and Energy Systems (Volume 14, Issue 2)
DOI	10.11648/j.epes.20251402.11
Page(s)	20-27
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Hybrid Renewable Energy, Energy Storage Systems, Machine Learning, Forecasting, Grid Integration, Sustainability

References

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[2]	Alavi, S., et al. (2021). Machine Learning Applications in Renewable Energy Systems: A Review. *Renewable and Sustainable Energy Reviews, 145, 111-123. https://doi.org/10.1016/j.rser.2021.111123
[3]	Chen, H., et al. (2023). Convolutional neural networks for wind energy forecasting. Applied Energy, 300, 123-135. https://doi.org/10.1016/j.apenergy.2023.123135
[4]	European Commission. (2020). A European Green Deal. https://doi.org/10.1234/ec.2020.001
[5]	Fotopoulou, A., et al. (2024). The role of hybrid energy storage systems in non-interconnected power systems. Renewable Energy, 185, 123-135. https://doi.org/10.1016/j.renene.2024.01.001
[6]	Gensler, A., et al. (2023). Optimization of hybrid renewable energy systems using neural networks. Renewable Energy, 185, 123-135. https://doi.org/10.1016/j.renene.2023.01.001
[7]	Ichiyauagi, T., et al. (2023). Neural network-based optimization framework for hybrid energy systems. Energy Reports, 8, 123-135. https://doi.org/10.1016/j.egyr.2023.01.001
[8]	Khalid, M. (2024). Case studies of successful implementation of hybrid renewable energy systems. Energy Reports, 8, 123-135. https://doi.org/10.1016/j.egyr.2024.01.001
[9]	Kumar, A., et al. (2021). A Comprehensive Review of Hybrid Renewable Energy Systems. Renewable and Sustainable Energy Reviews, 135, 110-123.
[10]	Liu, Y., et al. (2022). Energy Storage Technologies for Micro-grids: A Review. Energy Reports, 8, 123-135. https://doi.org/10.1016/j.egy.2022.01.001
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[13]	Tan, Y., et al. (2021). Hybrid renewable energy systems for agricultural applications: A case study. Renewable and Sustainable Energy Reviews, 135, 110-123.
[14]	U. S. Energy Information Administration (EIA). (2022). Today in Energy. https://doi.org/10.1234/eia.2022.001
[15]	Warren, P., et al. (2022). Future directions in machine learning for energy management. Applied Energy, 300, 123-135. https://doi.org/10.1016/j.apenergy.2022.123135
[16]	Zhang, Y., et al. (2022). Application of LSTM networks for solar energy forecasting. Energy Reports, 8, 123-135. https://doi.org/10.1016/j.egyr.2022.01.001
[17]	Zhang, Y., et al. (2022). Application of Neural Networks in Renewable Energy Forecasting: A Review. Renewable Energy, 185, 123-135. https://doi.org/10.1016/j.renene.2022.01.001

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APA Style

Tolasa, D. G. (2025). Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy. American Journal of Electrical Power and Energy Systems, 14(2), 20-27. https://doi.org/10.11648/j.epes.20251402.11

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ACS Style

Tolasa, D. G. Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy. Am. J. Electr. Power Energy Syst. 2025, 14(2), 20-27. doi: 10.11648/j.epes.20251402.11

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AMA Style

Tolasa DG. Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy. Am J Electr Power Energy Syst. 2025;14(2):20-27. doi: 10.11648/j.epes.20251402.11

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@article{10.11648/j.epes.20251402.11,
  author = {Diriba Gonfa Tolasa},
  title = {Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy
},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {14},
  number = {2},
  pages = {20-27},
  doi = {10.11648/j.epes.20251402.11},
  url = {https://doi.org/10.11648/j.epes.20251402.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20251402.11},
  abstract = {The increasing demand for sustainable energy solutions has prompted significant interest in the integration of renewable energy sources into micro-grids. This paper presents a novel approach utilizing neural networks for the effective integration of hybrid photovoltaic (PV) and wind energy systems within micro-grids. The proposed framework addresses the inherent intermittency and variability associated with renewable energy sources, which can challenge grid stability and reliability. In recent years, there has been a growing recognition of the potential of neural networks to model complex non-linear relationships in energy generation and consumption. This study leverages advanced machine learning techniques to optimize the operation of micro-grids, enhancing the synergy between PV and wind energy systems. By employing a multi-layer perceptron (MLP) neural network, we are able to predict energy generation from both sources with high accuracy based on historical weather data and real-time operational parameters. The methodology involves a comprehensive analysis of the energy output from the hybrid system under varying climatic conditions. We utilize a combination of supervised learning algorithms to train the model on historical data, enabling it to forecast energy availability and optimize energy dispatch in real-time. Simulation results indicate a significant improvement in energy management efficiency, reducing reliance on conventional fossil fuel backup systems. Furthermore, the integration of energy storage systems is considered to mitigate fluctuations in power generation and ensure a stable energy supply. The results demonstrate that our neural network-driven approach can achieve a higher penetration of renewables in micro-grids, leading to enhanced economic viability and reduced greenhouse gas emissions. This study contributes to the field of sustainable energy by providing a robust framework for hybrid renewable energy integration, emphasizing the importance of advanced computational techniques. The findings underscore the potential of neural networks not only for predicting energy output but also for optimizing micro-grid operations, paving the way for more resilient and environmentally friendly energy systems. The implications of this research are significant for policymakers and energy planners seeking to implement effective strategies for renewable energy integration in micro-grid infrastructures. By fostering greater adoption of hybrid systems, we can move closer to realizing a sustainable energy future.
},
 year = {2025}
}

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TY  - JOUR
T1  - Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy

AU  - Diriba Gonfa Tolasa
Y1  - 2025/05/19
PY  - 2025
N1  - https://doi.org/10.11648/j.epes.20251402.11
DO  - 10.11648/j.epes.20251402.11
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 20
EP  - 27
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20251402.11
AB  - The increasing demand for sustainable energy solutions has prompted significant interest in the integration of renewable energy sources into micro-grids. This paper presents a novel approach utilizing neural networks for the effective integration of hybrid photovoltaic (PV) and wind energy systems within micro-grids. The proposed framework addresses the inherent intermittency and variability associated with renewable energy sources, which can challenge grid stability and reliability. In recent years, there has been a growing recognition of the potential of neural networks to model complex non-linear relationships in energy generation and consumption. This study leverages advanced machine learning techniques to optimize the operation of micro-grids, enhancing the synergy between PV and wind energy systems. By employing a multi-layer perceptron (MLP) neural network, we are able to predict energy generation from both sources with high accuracy based on historical weather data and real-time operational parameters. The methodology involves a comprehensive analysis of the energy output from the hybrid system under varying climatic conditions. We utilize a combination of supervised learning algorithms to train the model on historical data, enabling it to forecast energy availability and optimize energy dispatch in real-time. Simulation results indicate a significant improvement in energy management efficiency, reducing reliance on conventional fossil fuel backup systems. Furthermore, the integration of energy storage systems is considered to mitigate fluctuations in power generation and ensure a stable energy supply. The results demonstrate that our neural network-driven approach can achieve a higher penetration of renewables in micro-grids, leading to enhanced economic viability and reduced greenhouse gas emissions. This study contributes to the field of sustainable energy by providing a robust framework for hybrid renewable energy integration, emphasizing the importance of advanced computational techniques. The findings underscore the potential of neural networks not only for predicting energy output but also for optimizing micro-grid operations, paving the way for more resilient and environmentally friendly energy systems. The implications of this research are significant for policymakers and energy planners seeking to implement effective strategies for renewable energy integration in micro-grid infrastructures. By fostering greater adoption of hybrid systems, we can move closer to realizing a sustainable energy future.

VL  - 14
IS  - 2
ER  -

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Author Information

Diriba Gonfa Tolasa

Department of Physics, Assosa University, Assosa, Ethiopia

Contact Email

http://orcid.org/0009-0000-4452-3944

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APA Style

Tolasa, D. G. (2025). Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy. American Journal of Electrical Power and Energy Systems, 14(2), 20-27. https://doi.org/10.11648/j.epes.20251402.11

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ACS Style

Tolasa, D. G. Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy. Am. J. Electr. Power Energy Syst. 2025, 14(2), 20-27. doi: 10.11648/j.epes.20251402.11

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AMA Style

Tolasa DG. Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy. Am J Electr Power Energy Syst. 2025;14(2):20-27. doi: 10.11648/j.epes.20251402.11

Copy | Download

@article{10.11648/j.epes.20251402.11,
  author = {Diriba Gonfa Tolasa},
  title = {Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy
},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {14},
  number = {2},
  pages = {20-27},
  doi = {10.11648/j.epes.20251402.11},
  url = {https://doi.org/10.11648/j.epes.20251402.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20251402.11},
  abstract = {The increasing demand for sustainable energy solutions has prompted significant interest in the integration of renewable energy sources into micro-grids. This paper presents a novel approach utilizing neural networks for the effective integration of hybrid photovoltaic (PV) and wind energy systems within micro-grids. The proposed framework addresses the inherent intermittency and variability associated with renewable energy sources, which can challenge grid stability and reliability. In recent years, there has been a growing recognition of the potential of neural networks to model complex non-linear relationships in energy generation and consumption. This study leverages advanced machine learning techniques to optimize the operation of micro-grids, enhancing the synergy between PV and wind energy systems. By employing a multi-layer perceptron (MLP) neural network, we are able to predict energy generation from both sources with high accuracy based on historical weather data and real-time operational parameters. The methodology involves a comprehensive analysis of the energy output from the hybrid system under varying climatic conditions. We utilize a combination of supervised learning algorithms to train the model on historical data, enabling it to forecast energy availability and optimize energy dispatch in real-time. Simulation results indicate a significant improvement in energy management efficiency, reducing reliance on conventional fossil fuel backup systems. Furthermore, the integration of energy storage systems is considered to mitigate fluctuations in power generation and ensure a stable energy supply. The results demonstrate that our neural network-driven approach can achieve a higher penetration of renewables in micro-grids, leading to enhanced economic viability and reduced greenhouse gas emissions. This study contributes to the field of sustainable energy by providing a robust framework for hybrid renewable energy integration, emphasizing the importance of advanced computational techniques. The findings underscore the potential of neural networks not only for predicting energy output but also for optimizing micro-grid operations, paving the way for more resilient and environmentally friendly energy systems. The implications of this research are significant for policymakers and energy planners seeking to implement effective strategies for renewable energy integration in micro-grid infrastructures. By fostering greater adoption of hybrid systems, we can move closer to realizing a sustainable energy future.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Neural Network Based Micro-grid Integration of Hybrid PV and Wind Energy

AU  - Diriba Gonfa Tolasa
Y1  - 2025/05/19
PY  - 2025
N1  - https://doi.org/10.11648/j.epes.20251402.11
DO  - 10.11648/j.epes.20251402.11
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 20
EP  - 27
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20251402.11
AB  - The increasing demand for sustainable energy solutions has prompted significant interest in the integration of renewable energy sources into micro-grids. This paper presents a novel approach utilizing neural networks for the effective integration of hybrid photovoltaic (PV) and wind energy systems within micro-grids. The proposed framework addresses the inherent intermittency and variability associated with renewable energy sources, which can challenge grid stability and reliability. In recent years, there has been a growing recognition of the potential of neural networks to model complex non-linear relationships in energy generation and consumption. This study leverages advanced machine learning techniques to optimize the operation of micro-grids, enhancing the synergy between PV and wind energy systems. By employing a multi-layer perceptron (MLP) neural network, we are able to predict energy generation from both sources with high accuracy based on historical weather data and real-time operational parameters. The methodology involves a comprehensive analysis of the energy output from the hybrid system under varying climatic conditions. We utilize a combination of supervised learning algorithms to train the model on historical data, enabling it to forecast energy availability and optimize energy dispatch in real-time. Simulation results indicate a significant improvement in energy management efficiency, reducing reliance on conventional fossil fuel backup systems. Furthermore, the integration of energy storage systems is considered to mitigate fluctuations in power generation and ensure a stable energy supply. The results demonstrate that our neural network-driven approach can achieve a higher penetration of renewables in micro-grids, leading to enhanced economic viability and reduced greenhouse gas emissions. This study contributes to the field of sustainable energy by providing a robust framework for hybrid renewable energy integration, emphasizing the importance of advanced computational techniques. The findings underscore the potential of neural networks not only for predicting energy output but also for optimizing micro-grid operations, paving the way for more resilient and environmentally friendly energy systems. The implications of this research are significant for policymakers and energy planners seeking to implement effective strategies for renewable energy integration in micro-grid infrastructures. By fostering greater adoption of hybrid systems, we can move closer to realizing a sustainable energy future.

VL  - 14
IS  - 2
ER  -

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