希尔伯特-黄变换
风速
均方误差
离散小波变换
相关系数
小波变换
风力发电
能量(信号处理)
数学
算法
计算机科学
小波
统计
气象学
人工智能
工程类
地理
电气工程
作者
Hassan Bashir,Muhammad Sibtain,Özge Hanay,Muhammad Imran Azam,- Quratulain,Snoober Saleem
出处
期刊:Energy
[Elsevier]
日期:2023-09-01
卷期号:278: 127933-127933
被引量:1
标识
DOI:10.1016/j.energy.2023.127933
摘要
Accurate wind speed forecasting (WSF) is important for effectively harnessing wind energy with clean and sustainable energy benefits. Therefore, this study develops different models established through the use of correlation analysis (CA) and decomposition techniques, Harris hawks optimization algorithm (HHO), and S2S (sequence2sequence) based spatial and temporal attention (STAt-S2S) for effective WSF. First, the CA selects variables of significant correlation with the wind speed data. In the next stage, improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN) and discrete wavelet transform with maximum overlap (MODWT) techniques are employed to decompose the components having significant correlation. Afterwards, HHO selects suitable features from the decomposed data. Finally, STA-S2S extracts spatial, temporal features and performs forecasting. The CA-ICEEMDAN–HHO–STAt-S2S and CA-ICEEMDAN-STAt-S2S models reveal better forecasting outcomes over the other standalone and hybrid foresting models. The RMSE, MAE, and sMAPE values presented by CA-ICEEMDAN-STAt-S2S are 0.639 m/s, 0.474 m/s and 15.710 m/s with NSE of 0.922. The lowest error values with the highest efficiency values of ICEEMDAN, HHO, and STAt-S2S-based hybrid models corroborate the feasibility of these models for WSF with equal applicability for similar time series applications.
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