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Global spatial suitability mapping of wind and solar systems using an explainable aI-based approach

An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weight...

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Main Authors: Sachit, Mourtadha Sarhan, Mohd Shafri, Helmi Zulhaidi, Abdullah, Ahmad Fikri, Mohd Rafie, Azmin Shakrine, Gibril, Mohamed Barakat A.
Format: Article
Published: Multidisciplinary Digital Publishing Institute 2022
Online Access:http://psasir.upm.edu.my/id/eprint/101636/
https://www.mdpi.com/2220-9964/11/8/422
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spelling my.upm.eprints.1016362023-06-16T20:26:09Z http://psasir.upm.edu.my/id/eprint/101636/ Global spatial suitability mapping of wind and solar systems using an explainable aI-based approach Sachit, Mourtadha Sarhan Mohd Shafri, Helmi Zulhaidi Abdullah, Ahmad Fikri Mohd Rafie, Azmin Shakrine Gibril, Mohamed Barakat A. An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weightings. This study presents a global wind and solar mapping approach based on eXplainable Artificial Intelligence (XAI). To the best of the author’s knowledge, the current study is the first attempt to create global maps for siting onshore wind and solar power systems and formulate novel weights for decision criteria. A total of 13 conditioning factors (independent variables) defined through a comprehensive literature review and multicollinearity analysis were assessed. Real-world renewable energy experiences (more than 55,000 on-site wind and solar plants worldwide) are exploited to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Then, the output of ML models was explained using SHapley Additive exPlanations (SHAP). RF outperformed SVM and MLP in both wind and solar modeling with an overall accuracy of 90% and 89%, kappa coefficient of 0.79 and 0.78, and area under the curve of 0.96 and 0.95, respectively. The high and very high suitability categories accounted for 23.2% (~26.84 million km2) of the site suitability map for wind power plants. In addition, they covered more encouraging areas (24.0% and 19.4%, respectively, equivalent to ~50.31 million km2) on the global map for hosting solar energy farms. SHAP interpretations were consistent with the Gini index indicating the dominance of the weights of technical and economic factors over the spatial assessment under consideration. This study provides support to decision-makers toward sustainable power planning worldwide. Multidisciplinary Digital Publishing Institute 2022-07-26 Article PeerReviewed Sachit, Mourtadha Sarhan and Mohd Shafri, Helmi Zulhaidi and Abdullah, Ahmad Fikri and Mohd Rafie, Azmin Shakrine and Gibril, Mohamed Barakat A. (2022) Global spatial suitability mapping of wind and solar systems using an explainable aI-based approach. ISPRS International Journal of Geo-Information, 11 (8). art. no. 422. pp. 1-26. ISSN 2220-9964 https://www.mdpi.com/2220-9964/11/8/422 10.3390/ijgi11080422
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weightings. This study presents a global wind and solar mapping approach based on eXplainable Artificial Intelligence (XAI). To the best of the author’s knowledge, the current study is the first attempt to create global maps for siting onshore wind and solar power systems and formulate novel weights for decision criteria. A total of 13 conditioning factors (independent variables) defined through a comprehensive literature review and multicollinearity analysis were assessed. Real-world renewable energy experiences (more than 55,000 on-site wind and solar plants worldwide) are exploited to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Then, the output of ML models was explained using SHapley Additive exPlanations (SHAP). RF outperformed SVM and MLP in both wind and solar modeling with an overall accuracy of 90% and 89%, kappa coefficient of 0.79 and 0.78, and area under the curve of 0.96 and 0.95, respectively. The high and very high suitability categories accounted for 23.2% (~26.84 million km2) of the site suitability map for wind power plants. In addition, they covered more encouraging areas (24.0% and 19.4%, respectively, equivalent to ~50.31 million km2) on the global map for hosting solar energy farms. SHAP interpretations were consistent with the Gini index indicating the dominance of the weights of technical and economic factors over the spatial assessment under consideration. This study provides support to decision-makers toward sustainable power planning worldwide.
format Article
author Sachit, Mourtadha Sarhan
Mohd Shafri, Helmi Zulhaidi
Abdullah, Ahmad Fikri
Mohd Rafie, Azmin Shakrine
Gibril, Mohamed Barakat A.
spellingShingle Sachit, Mourtadha Sarhan
Mohd Shafri, Helmi Zulhaidi
Abdullah, Ahmad Fikri
Mohd Rafie, Azmin Shakrine
Gibril, Mohamed Barakat A.
Global spatial suitability mapping of wind and solar systems using an explainable aI-based approach
author_facet Sachit, Mourtadha Sarhan
Mohd Shafri, Helmi Zulhaidi
Abdullah, Ahmad Fikri
Mohd Rafie, Azmin Shakrine
Gibril, Mohamed Barakat A.
author_sort Sachit, Mourtadha Sarhan
title Global spatial suitability mapping of wind and solar systems using an explainable aI-based approach
title_short Global spatial suitability mapping of wind and solar systems using an explainable aI-based approach
title_full Global spatial suitability mapping of wind and solar systems using an explainable aI-based approach
title_fullStr Global spatial suitability mapping of wind and solar systems using an explainable aI-based approach
title_full_unstemmed Global spatial suitability mapping of wind and solar systems using an explainable aI-based approach
title_sort global spatial suitability mapping of wind and solar systems using an explainable ai-based approach
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/101636/
https://www.mdpi.com/2220-9964/11/8/422
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score 13.211869

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