Kernel ridge regression with active learning for wind speed prediction

Short-term wind power prediction based on LSSVM–GSA model

“…With regard to the mathematical model for wind power prediction [3], method of physics, statistical method and intellectual learning method [4,5] are usually adopted. The basic principle of the physics method is to predict wind power by utilizing the turbine power curve combined with a numerical weather prediction system based on wind farm terrain and information about the wind-turbine [6].…”

A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction

“…To verify the performance of the proposed algorithms in this paper, three cases with data from three selected measurement stations are adopted, and the proposed new algorithms are compared with ARMA [12], ANN [19], KRReRSAL [23], and SMN model [29] methods. Three prediction error measures are …”

“…In addition to ANN models, SVR methods have been applied to predict wind speed through a nonlinear kernel function based predictive model within highedimensional feature space. Douak et al [23] presented three active learning methods to construct the training set applied to wind speed prediction based on the kernel ridge regression approach. An approach for longeterm wind speed prediction was developed in Yu et al [24] by integrating Gaussian mixture copula model and localized Gaussian process regression.…”

Multiple regression analysis of performance parameters of a binary cycle geothermal power plant

“…In addition, cooling loads of air conditioning (HVAC) systems are modeled for buildings (Lam et al, 1997;Ben-Nakhi and Mahmoud, 2004;Lam et al, 2010). Besides, regression analysis is often used to predict wind properties such as wind speed and direction (Salcedo-Sanz et al, 2011;Douak et al, 2012;Utsunomiya et al, 1998). Several authors (Carta et al, 2011;Amjady et al, 2011;Liu et al, 2013) studied estimation of power generation of a wind turbine.…”