Publicação
Suitability Mapping of Solar Power Plants Using an Explainable AI-Based Approach
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Vanneschi, Leonardo | |
| dc.contributor.advisor | Painho, Marco Octávio Trindade | |
| dc.contributor.advisor | Gould, Michael | |
| dc.contributor.author | Hewa, Rasanka Mangala de Silva Mawanane | |
| dc.date.accessioned | 2024-03-27T10:50:55Z | |
| dc.date.available | 2024-03-27T10:50:55Z | |
| dc.date.issued | 2024-03-01 | |
| dc.description | Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies | pt_PT |
| dc.description.abstract | The conventional approaches to identifying optimal locations for solar power plants are traditionally handled through Multi-Criteria Decision-Making (MCDM) techniques, which often suffer from subjectivity and lack of transparency. Although recent advancements in Machine Learning (ML) have offered potential solutions to MCDM-based methods, traditional ML models struggle to explain their predictions and operate without assuming that current solar power plants are in optimal locations. Thus, this research represents an integrated explainable AI approach with ML methods, aiming to enhance the transparency and comprehensibility of results and introduce a novel paradigm by employing the classified efficiency of existing plants, categorized into five classes, as the dependent variable for ML models, thereby challenging the prevalent assumption inherent in conventional ML models. Twelve independent variables selected through a literature review were used, along with the classified efficiency values, to train five ML models, namely, Random Forest (RF), Support Vector Machines (SVM), Multi-layer Perceptron (MLP), Decision tree (DT), and K-nearest neighbors (k-NN). Following the assessment of the models' accuracies, the RF model, which achieved 88% overall accuracy, was subsequently explained using SHapley Additive exPlanations (SHAP), revealing Solar Radiation (13%) and Cloud Index (12%) as the most influential variables for the resulting predictions. In comparison, Aspect (5%) was identified as the least significant parameter to the model predictions. The final solar suitability map produced with the superior RF model identified approximately 5% of the total land area in the USA as highly suitable for constructing solar power plants, ensuring optimal operational efficiency. Additionally, 55% of the land is moderately suitable for such establishments. Conversely, approximately 9.5% of the total land area, equivalent to 766,654 km2, is deemed permanently unsuitable for solar power plant construction. | pt_PT |
| dc.identifier.tid | 203561368 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/165508 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Renewable Energy | pt_PT |
| dc.subject | Geographical Information Systems | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Explainable Artificial Intelligence | pt_PT |
| dc.subject | Suitability Mapping | pt_PT |
| dc.subject | United States of America | pt_PT |
| dc.subject | SDG 7 - Affordable and clean energy | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.subject | SDG 11 - Sustainable cities and communities | pt_PT |
| dc.subject | SDG 12 - Responsible production and consumption | pt_PT |
| dc.subject | SDG 13 - Climate action | pt_PT |
| dc.title | Suitability Mapping of Solar Power Plants Using an Explainable AI-Based Approach | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Tecnologias Geoespaciais | pt_PT |