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Smart city article details
| Title | Solar Power Generation Forecasting In Smart Cities And Explanation Based On Explainable Ai |
|---|---|
| ID_Doc | 52236 |
| Authors | Petrosian O.; Zhang Y. |
| Year | 2024 |
| Published | Smart Cities, 7, 6 |
| DOI | http://dx.doi.org/10.3390/smartcities7060132 |
| Abstract | Highlights: What are the main findings? LightGBM, among the selected black-box models, was identified as requiring explanation due to its performance in solar power generation forecasting. “Distance from the Noon” and its interaction with “Sky Cover” were highlighted as the primary environmental factors influencing solar power generation. What is the implication of the main finding? Understanding the key environmental factors enables more accurate placement and optimization of solar power stations in smart cities. The use of Explainable AI provides valuable insights that can guide policymakers and engineers in enhancing solar energy infrastructure. 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. © 2024 by the authors. |
| Author Keywords | deep learning; ensemble learning; explainable artificial intelligence; solar power forecasting |
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