Li, J.J. and Zhao, L.F., 2020. Hydropower price prediction with the nonparametric statistics regression model. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 402–405. Coconut Creek (Florida), ISSN 0749-0208.

Predicting the Hydropower price is facing more challenges due to the market-based reform of China's electric power industry, and the commonly used prediction models cannot ensure accuracy and usability at the same time. Considering the rapid development and application of the nonparametric statistics theory, this paper attempted to use the nonparametric model to predict the on-grid price. Firstly, the nonparametric regression is used to fit the price curve and obtain the optimal kernel function. Secondly, the semi-parametric time series method is used to estimate the on-grid price. According to the prediction analysis of the PJM level price in the United States, nonparametric estimation has notable advantages in various prediction indexes compared with the linear regression estimation method and the grey model prediction method. The nonparametric model is not only convenient to use, but also widely applicable to the long-term and short-term prediction of different power markets.