XAI, which allows users to see inside the "black box" of AI models, has been instrumental in fields like computer vision. Now, researchers are extending its use to sectors like healthcare, transportation, and finance, where trust and transparency are paramount. In the context of wind power, XAI's ability to clarify the decision-making process behind forecasts could significantly reduce uncertainties that grid operators face when integrating renewable energy into power systems.
Prof. Fernando Porte-Agel, head of WiRE, emphasizes the importance of reliable wind power predictions for smart grid integration. "Before grid operators can effectively integrate wind power into their smart grids, they need reliable daily forecasts of wind energy generation with a low margin of error," he states. "Inaccurate forecasts mean grid operators have to compensate at the last minute, often using more expensive fossil fuel-based energy."
Although traditional models that rely on fluid dynamics and weather data are used to predict wind power output, they still carry a margin of error. AI has shown promise in refining these predictions by processing vast amounts of data to identify correlations between weather variables and wind turbine performance. However, most AI models remain "black boxes," leaving operators uncertain about how forecasts are generated. XAI aims to resolve this by offering visibility into the models' inner workings, thus making forecasts more trustworthy.
For their study, the researchers trained a neural network using key weather variables - such as wind speed, wind direction, air pressure, and temperature - alongside data from global wind farms, including those in Switzerland. Wenlong Liao, the study's lead author, explains that they developed four XAI techniques to interpret data and established metrics to assess the reliability of these interpretations. "We tailored four XAI techniques and developed metrics for determining whether a technique's interpretation of the data is reliable," says Liao.
Metrics are essential tools in machine learning that assess model performance, such as whether the relationship between variables is causation or correlation. In their study, the team defined metrics to evaluate XAI techniques' trustworthiness and showed that the models could be made more accurate by excluding certain variables, thus simplifying the forecasting process without sacrificing reliability.
Jiannong Fang, co-author of the study, believes this breakthrough can make wind power more competitive. "Power system operators won't feel very comfortable relying on wind power if they don't understand the internal mechanisms that their forecasting models are based on," he explains. "But with the XAI-based approach, models can be diagnosed and upgraded, generating more reliable forecasts of daily wind power fluctuations."
The research findings can help improve the stability and cost-efficiency of wind energy systems, thus supporting the global shift towards renewable energy sources.
Research Report:Can we trust explainable artificial intelligence in wind power forecasting?
Related Links
Swiss Federal Technology Institute of Lausanne
Wind Energy News at Wind Daily
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