Forecasting model for wind power integrating least squares support vector machine, singular spectrum analysis, deep belief network, and locality‐sensitive hashing

Wind Energy ◽  
2020 ◽  
Vol 23 (2) ◽  
pp. 235-257 ◽  
Author(s):  
Arslan Habib ◽  
Rabeh Abbassi ◽  
Andrés Julián Aristizábal ◽  
Abdelkader Abbassi
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Wind Power ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Locality Sensitive Hashing ◽  
Support Vector ◽  
Forecasting Model ◽  
Belief Network ◽  
Singular Spectrum

Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

2013 ◽  
Vol 64 (1) ◽  
pp. 280-289 ◽  
Author(s):  
Liangjun Xie ◽  
Nong Gu ◽  
Dalong Li ◽  
Zhiqiang Cao ◽  
Min Tan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Control Chart ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Support Vector ◽  
Concurrent Control ◽  
Singular Spectrum ◽  
Control Chart Patterns ◽  
Chart Patterns ◽  
Control Chart Patterns Recognition

scholarly journals Short-term local prediction of wind speed and wind power based on singular spectrum analysis and locality-sensitive hashing

2018 ◽  
Vol 6 (2) ◽  
pp. 317-329 ◽  
Author(s):  
Ling LIU ◽  
Tianyao JI ◽  
Mengshi LI ◽  
Ziming CHEN ◽  
Qinghua WU
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Locality Sensitive Hashing ◽  
Short Term ◽  
Singular Spectrum ◽  
Local Prediction

scholarly journals A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power

Energies ◽  
2012 ◽  
Vol 5 (9) ◽  
pp. 3329-3346 ◽  
Author(s):  
Qian Zhang ◽  
Kin Keung Lai ◽  
Dongxiao Niu ◽  
Qiang Wang ◽  
Xuebin Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Wind Power ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Model Based ◽  
Fuzzy Group

Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting

2021 ◽  
Vol 35 (3) ◽  
pp. 847-868
Author(s):  
Quoc Bao Pham ◽  
Tao-Chang Yang ◽  
Chen-Min Kuo ◽  
Hung-Wei Tseng ◽  
Pao-Shan Yu
Keyword(s):  
Support Vector Machine ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Least Square ◽  
Support Vector ◽  
Drought Forecasting ◽  
Singular Spectrum ◽  
Improve Accuracy

scholarly journals Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting

2020 ◽  
Vol 10 (9) ◽  
pp. 3224 ◽  
Author(s):  
Pa Ousman Bojang ◽  
Tao-Chang Yang ◽  
Quoc Bao Pham ◽  
Pao-Shan Yu
Keyword(s):  
Machine Learning ◽  
Water Resources ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Hybrid Models ◽  
Monthly Rainfall ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Singular Spectrum ◽  
Standard Models

Monthly rainfall forecasts can be translated into monthly runoff predictions that could support water resources planning and management activities. Therefore, development of monthly rainfall forecasting models in reservoir watersheds is essential for generating future rainfall amounts as an input to a water-resources-system simulation model to predict water shortage conditions. This research aims to examine the reliability of linking a data preprocessing method (singular spectrum analysis, SSA) with machine learning, least-squares support vector regression (LS-SVR), and random forest (RF), for monthly rainfall forecasting in two reservoir watersheds (Deji and Shihmen reservoir watersheds) located in Taiwan. Merging SSA with LS-SVR and RF, the hybrid models (SSA-LSSVR and SSA-RF) were developed and compared with the standard models (LS-SVR and RF). The proposed models were calibrated and validated using the watersheds’ observed areal monthly rainfalls separated into 70 percent of data for calibration and 30 percent of data for validation. Model performances were evaluated using two accuracy measures, root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). Results show that the hybrid models could efficiently forecast monthly rainfalls. Nonetheless, the performances of the hybrid models vary in both watersheds which suggests that prior knowledge about the watershed’s hydrological behavior would be helpful to implement the appropriate model. Overall, the hybrid models significantly surpass the standard models for the two studied watersheds, which indicates that the proposed models are a prudent modeling approach that could be employed in the current research regions for monthly rainfall forecasting.

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