Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads

2021 ◽  
pp. 107297
Author(s):  
Zichen Zhang ◽  
Wei-Chiang Hong
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Variational Mode Decomposition ◽  
Grey Wolf ◽  
Mode Decomposition ◽  
Electric Loads

scholarly journals Two-Stage Hybrid Machine Learning Model for High-Frequency Intraday Bitcoin Price Prediction Based on Technical Indicators, Variational Mode Decomposition, and Support Vector Regression

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Samuel Asante Gyamerah
Keyword(s):  
Support Vector Regression ◽  
Technical Analysis ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Two Stage ◽  
Technical Indicators ◽  
Price Prediction ◽  
Price Series ◽  
Mode Decomposition ◽  
Statistical Trends

Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two-stage Bitcoin price prediction model by combining the advantage of variational mode decomposition (VMD) and technical analysis. VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past trading activity and price changes to construct technical indicators. The support vector regression (SVR) accepts input from a hybrid of technical indicators (TI) and reconstructed variational mode functions (rVMF). The model is trained, validated, and tested in a period characterized by unprecedented economic turmoil due to the COVID-19 pandemic, allowing the evaluation of the model in the presence of the pandemic. The constructed hybrid model outperforms the single SVR model that uses only TI and rVMF as features. The ability to predict a minute intraday Bitcoin price has a huge propensity to reduce investors’ exposure to risk and provides better assurances of annualized returns.

scholarly journals Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility

2021 ◽  
Vol 13 (24) ◽  
pp. 4966
Author(s):  
Ru Liu ◽  
Jianbing Peng ◽  
Yanqiu Leng ◽  
Saro Lee ◽  
Mahdi Panahi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Firefly Algorithm ◽  
Spatial Prediction ◽  
Training Dataset ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Conditioning Factors

Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency ratio (FR) preprocessed. Therefore, a landslide inventory composed of 140 landslides and 16 landslide conditioning factors is compiled as a landslide database. Among these landslides, 70% (98) landslides were randomly selected as the training dataset of the model, and the other landslides (42) were used to verify the model. The 16 landslide conditioning factors include elevation, slope, aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), landslide, rainfall, soil and lithology. The conditioning factors selection and spatial correlation analysis were carried out by using the correlation attribute evaluation (CAE) method and the frequency ratio (FR) algorithm. The area under the receiver operating characteristic curve (AUROC) and kappa data of the training dataset and validation dataset are used to evaluate the prediction ability and the relationship between the advantages and disadvantages of landslide susceptibility maps. The results show that the SVR-GWO model (AUROC = 0.854) has the best performance in landslide spatial prediction, followed by the SVR-FA (AUROC = 0.838) and SVR models (AUROC = 0.818). The hybrid models of SVR-GWO and SVR-FA improve the performance of the single SVR model, and all three models have good prospects for regional-scale landslide spatial modeling.

scholarly journals Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4890
Author(s):  
Mengran Zhou ◽  
Tianyu Hu ◽  
Kai Bian ◽  
Wenhao Lai ◽  
Feng Hu ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Support Vector ◽  
Electric Load ◽  
Variational Mode Decomposition ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Short Term ◽  
Mode Decomposition ◽  
The Impact ◽  
Electric Load Forecasting

Short-term electric load forecasting plays a significant role in the safe and stable operation of the LO system and power market transactions. In recent years, with the development of new energy sources, more and more sources have been integrated into the grid. This has posed a serious challenge to short-term electric load forecasting. Focusing on load series with non-linear and time-varying characteristics, an approach to short-term electric load forecasting using a “decomposition and ensemble” framework is proposed in this paper. The method is verified using hourly load data from Oslo and the surrounding areas of Norway. First, the load series is decomposed into five components by variational mode decomposition (VMD). Second, a support vector regression (SVR) forecasting model is established for the five components to predict the electric load components, and the grey wolf optimization (GWO) algorithm is used to optimize the cost and gamma parameters of SVR. Finally, the predicted values of the five components are superimposed to obtain the final electric load forecasting results. In this paper, the proposed method is compared with GWO-SVR without modal decomposition and using empirical mode decomposition (EMD) to test the impact of VMD on prediction. This paper also compares the proposed method with the SVR model using VMD and other optimization algorithms. The four evaluation indexes of the proposed method are optimal: MAE is 71.65 MW, MAPE is 1.41%, MSE is 10,461.32, and R2 is 0.9834. This indicates that the proposed method has a good application prospect for short-term electric load forecasting.

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