Wind speed forecasting using deep learning and preprocessing techniques

ABSTRACT

Most forecasting algorithms are tuned to a specific location or dataset and will not perform well in other situations. Some wind speed data might contain outliers, missed values, or noise which affects the forecasting performance tremendously. This paper proposes a hybrid-forecasting model that includes pre-processing and deep learning techniques to bridge the gap in developing a generic forecasting algorithm that is unspecific of location or dataset. The proposed model includes a preprocessing part that consists of missed value and outlier handling, decomposition, Kalman filtering, and smoothing. This is an important step because no matter how accurate the forecasting model is, results will vary tremendously if the dataset is corrupted. In addition to that, three different deep-base learning algorithms RNN, GRU, and LSTM will be used based on the characteristics of each subseries to reduce the complexity of the overall forecasting model. The proposed model performed the best across the seven tested sites from different locations with different climates and geography. Compared to other forecasting models such as LSTM standalone and EWT-LSTM, a performance improvement in accuracy by 50% as well as a 25% reduction in processing time was achieved with the proposed forecasting model

KEYWORDS:

• Artificial neural intelligence
• wind speed forecasting
• hybrid forecasting
• data preprocessing
• outlier detection
• Kalman smoothing
• deep neural network

Nomenclature

ANN	=	Artificial Neural Networks
ARMA	=	Auto-Regressive Moving Average
ARIMA	=	Auto-Regressive Integrated Moving Average
BPNN	=	Back Propagation Neural Network
BiLSTM	=	Bi-directional long short-term memory
DNN	=	Deep Neural Network
ELM	=	Extreme Learning Machine
EMD	=	Empirical Modal Distribution
EEMD	=	Ensemble Empirical Mode Decomposition
EWT	=	Empirical wavelets transform
GRU	=	Gated recurrent unit
GM	=	Gray Model
GRNN	=	Generalized Regression Neural Network
IQR	=	Inter-Quartile Range
LSTM	=	Long short-term memory
MODA	=	Multi-Objective Dragonfly Algorithm
MOMVO	=	Multi-Objective Multi-Verse optimizer
NWP	=	Numerical Weather Prediction
RNN	=	Recurrent neural network (RNN)
SVM	=	Support Vector Machine
SSA	=	Singular spectrum analysis
SSEA	=	Stacked sparse autoencoder
WD	=	Wavelet Decomposition
WPD	=	Wavelets Packet Decomposition

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15435075.2023.2228878