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Renewable energy is essential for energy security and global warmingmitigation. However, power generation from renewable energy sources isuncertain due to volatile weather conditions and complex equipment operations.To improve equipment's operation efficiency, it is important to understand andcharacterize the uncertainty in renewable power generation. In this paper, weproposed a conditional kernel density estimation method to model thedistribution of equipment's power output given any weather conditions. Itexplicitly accounts for the temporal dependence in the data stream and uses aniterative procedure to reduce the bias in kernel density estimation. Comparedwith existing literature, our approach is especially useful for the purposes ofequipment condition monitoring or short-term renewable energy forecasting,where the data dependence plays a more significant role. We demonstrate ourmethod and compare it with alternatives through real applications.

Conditional Kernel Density Estimation Considering Autocorrelation for Renewable Energy Probabilistic Modeling