Gaining insight into solar photovoltaic power generation forecasting utilizing explainable artificial intelligence tools
| Autor: | Umit Cali, Vinayak Sharma, Ozgur Guler, Murat Kuzlu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
business.industry Computer science 020209 energy Photovoltaic system Explainable artificial intelligence (XAI) solar PV power generation forecasting General Engineering Energy forecasting 02 engineering and technology Transparency (human–computer interaction) Field (computer science) Predictive maintenance Demand response Smart grid Electricity generation explainability and transparency 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 |
| Zdroj: | IEEE Access IEEE Access, Vol 8, Pp 187814-187823 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.3031477 |
| Popis: | Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a “black-box” due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This article presents several use cases of solar PV energy forecasting using XAI tools, such as LIME, SHAP, and ELI5, which can contribute to adopting XAI tools for smart grid applications. Understanding the inner workings of a prediction model based on AI can give insights into the application field. Such insight can provide improvements to the solar PV forecasting models and point out relevant parameters. |
| Databáze: | OpenAIRE |
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