Forecasting Solar Activities based on Sunspot Number Using Support Vector Regression (SVR)

Solar Power Forecasting Using Support Vector Regression

Generation and load balance is required in the economic scheduling of generating units in the smart grid. Variable energy generations particularly from wind and solar energy resources are witnessing a rapid boost, and, it is anticipated that with a certain level of their penetration, they become noteworthy sources of uncertainty. As in the case of load demand, energy forecasting can also be use...

Tehran Stock Price Modeling and Forecasting Using Support Vector Regression (SVR) and Its Comparison with the Classic Model ARIMA

Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected...

Support Vector Regression in Forecasting

Support Vector Regression (SVR), a category for Support Vector Machine (SVM) attempts to minimize the generalization error bound so as to achieve generalized performance. Regression is that of finding a function which approximates mapping from an input domain to the real numbers on the basis of a training sample. Support vector regression is the natural extension of large margin kernel methods ...

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the se...