Conditional Kernel Density Estimation Considering Autocorrelation for Renewable Energy Probabilistic Modeling

Renewable energy is essential for energy security and global warming mitigation. However, renewable power generation is uncertain due to volatile weather conditions and complex equipment operations. It is therefore important to understand and characterize the uncertainty in renewable power generation to improve operational efficiency. In this paper, we proposed a novel conditional density estimation method to model the distribution of power generation under various weather conditions. It explicitly accounted for the temporal dependence in the data stream and used an iterative procedure to reduce the bias in conventional density estimation. Compared with existing literature, our approach is especially useful for the purpose of short-term modeling, where the temporal dependence plays a more significant role. We demonstrate our method and compare it with alternatives through real applications.