Wind power prediction based on wind speed forecast using hidden Markov model

Abstract

This study examines a new approach for short‐term wind speed and power forecasting based on the mixture of Gaussian hidden Markov models (MoG‐HMMs). The proposed approach focuses on the characteristics of wind speed and power in the consecutive hours of previous days. The proposed method is carried out in two steps. In the first step, for the hourly prediction of wind speed, several wind speed features are employed in MoG‐HMM, and in the second step, the results obtained from the first step along with their characteristics and wind power features are used to predict wind power estimation. To increase the prediction accuracy, the data used in each step are classified, and then for each class, one HMM with its specific parameters is used. The performance of the proposed approach is examined using real NREL data. The results show that the proposed method is more precise than other examined methods.

Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting

“…Statistical models, such as auto-regressive (AR) models (Zhang et al, 2016), AR integrated moving average (ARIMA) models (Liu et al, 2021;Zhang et al, 2020), and others (Ghasvarian Jahromi et al, 2023), are capable of capturing the linear properties of the data in general. Currently, advanced AI methods like extreme learning machine (ELM) (Hua et al, 2022) and long shortterm memory (Huang et al, 2022) have emerged as potent tools in various domains.…”

“…Statistical models, such as auto‐regressive (AR) models (Zhang et al, 2016), AR integrated moving average (ARIMA) models (Liu et al, 2021; Zhang et al, 2020), and others (Ghasvarian Jahromi et al, 2023), are capable of capturing the linear properties of the data in general.…”