Short-Term Wind-Speed Forecasting Using Kernel Spectral Hidden Markov Models
This work addresses wind-speed prediction for renewable energy applications, but it is incremental as it applies an existing method to a specific domain.
The paper tackled short-term wind-speed forecasting by applying a kernel spectral hidden Markov model (KSHMM) to time series data, resulting in performance that was comparable or better than other machine learning methods on wind-speed data from the National Renewable Energy Laboratory.
In machine learning, a nonparametric forecasting algorithm for time series data has been proposed, called the kernel spectral hidden Markov model (KSHMM). In this paper, we propose a technique for short-term wind-speed prediction based on KSHMM. We numerically compared the performance of our KSHMM-based forecasting technique to other techniques with machine learning, using wind-speed data offered by the National Renewable Energy Laboratory. Our results demonstrate that, compared to these methods, the proposed technique offers comparable or better performance.