Modeling and forecasting using support vector regression and chaotic algorithms/applied study

In energy management, providing accurate findings to anticipate electrical load consumption is critical. The goal of this study is to develop methods for predicting electrical load, including artificial intelligence, neural network, ARIMA models, Bayesian models, and regression models. This paper pr...

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Bibliographic Details
Main Authors: El-Sadah, Huda Abd, Al-Thalabi, Sahera Hussein Zain
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Artificial intelligence
Autoregressive models
Electrical loads
Energy management
Evolutionary algorithms
Forecasting
Global optimization
Local optimization
Neural networks
Optimization
Parameters
Regression models
Statistical analysis
Support vector machines
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Summary:In energy management, providing accurate findings to anticipate electrical load consumption is critical. The goal of this study is to develop methods for predicting electrical load, including artificial intelligence, neural network, ARIMA models, Bayesian models, and regression models. This paper proposes a new support vector regression application with the chaotic algorithms for electrical load forecasting in the southern region (Basra, Maysan, Dhi Qar, Muthanna), When choosing three parameters of a model (SVR) using evolutionary algorithms, you often encounter problems of early convergence, slowly reaching the solution of global optimization or falling into local optimization, and to overcome early local optimization in determining three parameters of the model (SVR) a chaotic algorithm is used. The 1980-2019 electrical load was used as a data set, and the results (CGASVR) were compared with (CPSOSVR) and (CIASVR) to choose the best form of electrical load forecasting where results show that the CGASVR model is more superior and efficient based on MSE, MAE, MAPE and MPE.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0119575