Wind speed forecasting using deep learning and preprocessing techniques

TY - JOUR

T1 - Wind speed forecasting using deep learning and preprocessing techniques

AU - Ammar, Elham

AU - Xydis, George

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Artificial neural intelligence

KW - data preprocessing

KW - deep neural network

KW - hybrid forecasting

KW - Kalman smoothing

KW - outlier detection

KW - wind speed forecasting

UR - http://www.scopus.com/inward/record.url?scp=85164126039&partnerID=8YFLogxK

U2 - 10.1080/15435075.2023.2228878

DO - 10.1080/15435075.2023.2228878

M3 - Journal article

AN - SCOPUS:85164126039

SN - 1543-5075

VL - 21

SP - 988

EP - 1016

JO - International Journal of Green Energy

JF - International Journal of Green Energy

IS - 5

ER -