Solar Power Generation Forecasting in Smart Cities and Explanation Based on Explainable AI
The application of black-box models, namely ensemble and deep learning, has significantly advanced the effectiveness of solar power generation forecasting. However, these models lack explainability, which hinders comprehensive investigations into environmental influences. To address this limitation,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Smart Cities |
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| Online Access: | https://www.mdpi.com/2624-6511/7/6/132 |
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| author | Ovanes Petrosian Yuyi Zhang |
| author_facet | Ovanes Petrosian Yuyi Zhang |
| author_sort | Ovanes Petrosian |
| collection | DOAJ |
| description | The application of black-box models, namely ensemble and deep learning, has significantly advanced the effectiveness of solar power generation forecasting. However, these models lack explainability, which hinders comprehensive investigations into environmental influences. To address this limitation, we employ explainable artificial intelligence (XAI) techniques to enhance the interpretability of these black-box models, while ensuring their predictive accuracy. We carefully selected 10 prominent black-box models and deployed them using real solar power datasets. Within the field of artificial intelligence, it is crucial to adhere to standardized usage procedures to guarantee unbiased performance evaluations. Consequently, our investigation identifies LightGBM as the model that requires explanation. In a practical engineering context, we utilize XAI methods to extract understandable insights from the selected model, shedding light on the varying degrees of impact exerted by diverse environmental factors on solar power generation. This approach facilitates a nuanced analysis of the influence of the environment. Our findings underscore the significance of “Distance from the Noon” as the primary factor influencing solar power generation, which exhibits a clear interaction with “Sky Cover.” By leveraging the outcomes of our analyses, we propose optimal locations for solar power stations, thereby offering a tangible pathway for the practical. |
| format | Article |
| id | doaj-art-df4dc5a3b40d4b49bf50d06a00525f03 |
| institution | DOAJ |
| issn | 2624-6511 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj-art-df4dc5a3b40d4b49bf50d06a00525f032025-08-20T02:43:46ZengMDPI AGSmart Cities2624-65112024-11-01763388341110.3390/smartcities7060132Solar Power Generation Forecasting in Smart Cities and Explanation Based on Explainable AIOvanes Petrosian0Yuyi Zhang1St. Petersburg State University, 7-9 Universitetskaya Embankment, St Petersburg 199034, RussiaFaculty of Applied Mathematics-Control Processes, St. Petersburg State University, Universitetskiy Prospekt, 35, St Petersburg 198504, RussiaThe application of black-box models, namely ensemble and deep learning, has significantly advanced the effectiveness of solar power generation forecasting. However, these models lack explainability, which hinders comprehensive investigations into environmental influences. To address this limitation, we employ explainable artificial intelligence (XAI) techniques to enhance the interpretability of these black-box models, while ensuring their predictive accuracy. We carefully selected 10 prominent black-box models and deployed them using real solar power datasets. Within the field of artificial intelligence, it is crucial to adhere to standardized usage procedures to guarantee unbiased performance evaluations. Consequently, our investigation identifies LightGBM as the model that requires explanation. In a practical engineering context, we utilize XAI methods to extract understandable insights from the selected model, shedding light on the varying degrees of impact exerted by diverse environmental factors on solar power generation. This approach facilitates a nuanced analysis of the influence of the environment. Our findings underscore the significance of “Distance from the Noon” as the primary factor influencing solar power generation, which exhibits a clear interaction with “Sky Cover.” By leveraging the outcomes of our analyses, we propose optimal locations for solar power stations, thereby offering a tangible pathway for the practical.https://www.mdpi.com/2624-6511/7/6/132explainable artificial intelligencesolar power forecastingensemble learningdeep learning |
| spellingShingle | Ovanes Petrosian Yuyi Zhang Solar Power Generation Forecasting in Smart Cities and Explanation Based on Explainable AI Smart Cities explainable artificial intelligence solar power forecasting ensemble learning deep learning |
| title | Solar Power Generation Forecasting in Smart Cities and Explanation Based on Explainable AI |
| title_full | Solar Power Generation Forecasting in Smart Cities and Explanation Based on Explainable AI |
| title_fullStr | Solar Power Generation Forecasting in Smart Cities and Explanation Based on Explainable AI |
| title_full_unstemmed | Solar Power Generation Forecasting in Smart Cities and Explanation Based on Explainable AI |
| title_short | Solar Power Generation Forecasting in Smart Cities and Explanation Based on Explainable AI |
| title_sort | solar power generation forecasting in smart cities and explanation based on explainable ai |
| topic | explainable artificial intelligence solar power forecasting ensemble learning deep learning |
| url | https://www.mdpi.com/2624-6511/7/6/132 |
| work_keys_str_mv | AT ovanespetrosian solarpowergenerationforecastinginsmartcitiesandexplanationbasedonexplainableai AT yuyizhang solarpowergenerationforecastinginsmartcitiesandexplanationbasedonexplainableai |