Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models DOI Creative Commons

Rami Al-Hajj,

Gholamreza Oskrochi, Mohamad M. Fouad

et al.

Mathematical Biosciences & Engineering, Journal Year: 2024, Volume and Issue: 22(1), P. 23 - 51

Published: Jan. 1, 2024

<p>Forecasting wind speed plays an increasingly essential role in the energy industry. However, is uncertain with high changeability and dependency on weather conditions. Variability of directly influenced by fluctuation unpredictability speed. Traditional prediction methods provide deterministic forecasting that fails to estimate uncertainties associated predictions. Modeling those important reliable information when uncertainty level increases. Models for estimating intervals do not differentiate between daytime nighttime shifts, which can affect performance probabilistic forecasting. In this paper, we introduce a framework short-term The designed incorporates independent machine learning (ML) models point interval during respectively. First, feature selection techniques were applied maintain most relevant parameters datasets Second, support vector regressors (SVRs) used predict 10 minutes ahead. After that, incorporated non-parametric kernel density estimation (KDE) method statistically synthesize errors (PI) several confidence levels. simulation results validated effectiveness our demonstrated it generate are satisfactory all evaluation criteria. This verifies validity feasibility hypothesis separating data sets these types predictions.</p>

Language: Английский

Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S DOI
Xiaoying Sun, Haizhong Liu

Energy, Journal Year: 2024, Volume and Issue: 305, P. 132228 - 132228

Published: Oct. 1, 2024

Language: Английский

Citations

23
Interpretable wind power forecasting combing seasonal-trend representations learning with temporal fusion transformers architecture DOI
Zhewen Niu, Xiaoqing Han,

Dongxia Zhang

et al.

Energy, Journal Year: 2024, Volume and Issue: 306, P. 132482 - 132482

Published: July 17, 2024

Language: Английский

Citations

5
Localized Global Time Series Forecasting Models Using Evolutionary Neighbor‐Aided Deep Clustering Method DOI Open Access
Hossein Abbasimehr, Ali Noshad

Journal of Forecasting, Journal Year: 2025, Volume and Issue: unknown

Published: March 2, 2025

ABSTRACT Global forecasting models (GFMs) have become essential in time series prediction, as they enable cross‐learning across multiple series. Although GFMs consistently outperformed univariate approaches, their performance decreases when applied to heterogeneous datasets, such those found economic and financial applications. Clustering techniques been used create homogeneous clusters. However, the main limitations of current clustering‐based are follows: (1) employing handcrafted features instead deep learning (2) there is no guarantee that resulting clusters optimal terms prediction accuracy. To address these limitations, we propose a novel clustering model jointly optimizes The proposed method simultaneously reconstruction, clustering, losses ensure optimized for accurate forecasting. In addition, it employs neighbor‐aided autoencoder capture cluster‐oriented representations, leveraging neighboring improve feature learning. Furthermore, incorporate an evolutionary component, which iteratively refines through crossover mutation find We evaluate our on eight publicly available datasets considering various state‐of‐the‐art benchmarks. Results indicate all with 2620 series, obtains lowest mean symmetric absolute percentage error (sMAPE) 14.90, surpassing baseline (15.15). It exhibits enhancements 1.28, 0.70, 2.29 sMAPE relative DeepAR, N‐BEATS, transformer, respectively. demonstrates improvements compared existing global models. source code made at https://github.com/alinowshad/Evolutionary‐Neighbor‐Aided‐Deep‐Clustering‐DEEPEN .

Language: Английский

Citations

0
Wind speed forecasting using a combined deep learning model with slime mould optimization DOI
K. Natarajan, Jai Govind Singh

International Journal of Green Energy, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: April 17, 2025

Language: Английский

Citations

0
STGATN: a wind speed forecasting method based on geospatial dependency DOI Creative Commons
Xingtong Ge, Ling Peng, Yi Yang

et al.

International Journal of Digital Earth, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 28, 2025

Language: Английский

Citations

0
Forecasting maximal and minimal air temperatures using explainable machine learning: Shapley additive explanation versus local interpretable model-agnostic explanations DOI
Noureddine Daif, Fabio Di Nunno, Francesco Granata

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Language: Английский

Citations

0
DMPR: A Novel Wind Speed Forecasting Model Based on Optimized Decomposition, Multi-objective Feature Selection, and Patch-Based RNN DOI
Chenhao Cai,

Leyao Zhang,

Jianguo Zhou

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 133277 - 133277

Published: Sept. 1, 2024

Language: Английский

Citations

3
Social media-based multi-modal ensemble framework for forecasting soybean futures price DOI

Wuyue An,

Lin Wang, Yu‐Rong Zeng

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109439 - 109439

Published: Sept. 20, 2024

Language: Английский

Citations

2
A spatial transfer-based hybrid model for wind speed forecasting DOI
Xin Chen, Xiaoling Ye,

Jian Shi

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 133920 - 133920

Published: Nov. 1, 2024

Language: Английский

Citations

1
A novel analysis of random forest regression model for wind speed forecasting DOI Creative Commons

J Sathyaraj,

V. Sankardoss

Cogent Engineering, Journal Year: 2024, Volume and Issue: 11(1)

Published: Dec. 6, 2024

This article uses a random forest regression (RFR) model to analyze wind speed forecasting. Wind energy is one of the more critical potentials in renewable sources for producing clean and safe environment. Accurate stable forecasting essential improving efficiency turbines, guaranteeing power balance, economic dispatch systems ensuring equipment safety. Previous researchers have attempted address these issues less prediction performance lack interpretable analysis. study aims develop machine learning (ML) models, such as neural networks (NNs), linear (LR), support vector (SVR), decision tree (DTR), K-nearest neighbors (K-NN), extreme gradient boosting RFR. Six evaluation criteria are applied estimate ML model: mean squared error, root absolute error (MAE), percentage normalized average squares coefficient determination. The experimental results show RFR achieves better accuracy than other models. from was NMSE = 0.003, MAE 0.049, MSE 0.033, RMSE 0.182, MAPE 1.180 R2 0.996. Precise predictions various industries, aviation, shipping generation.

Language: Английский

Citations

1