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A first order vector autoregression topology was used to model and predict Turkey's net electricity consumption in the future. Input variables for the model were the annual values of electricity consumption along with four demographic and economic indicators such as, population, gross domestic product, imports and exports. Output variables were the one-step-ahead values of the same variables. First, polynomial regressions were used to determine and remove the trend components of all these five variables. Then, principal components regression method was applied to evaluate the coefficients of the vector autoregression model. Electricity consumption of Turkey was modeled using annual data from 1970 to 2016 and the model was used to predict future consumption values until year 2030. Singular value decomposition was used to determine the number of important dimensions in the data. This approach yielded a significant reduction in the dimensionality of the problem and thus provided robustness to the predictions. The results showed the feasibility of applying principal components regression method to vector autoregression model for electricity consumption prediction. [ABSTRACT FROM AUTHOR]

This paper proposes artificial neural network and fuzzy system-based extreme learning machines (ELM) for offline and online modeling of U-tube steam generators (UTSG). Water level of UTSG systems is predicted in a one-step-ahead fashion using nonlinear autoregressive with exogenous input (NARX) topology. Modeling data are generated using a well-known and widely accepted dynamic model reported in the literature. Model performances are analyzed with different number of neurons for the neural network and with different number of rules for the fuzzy system. UTSG models are built at different reactor power levels as well as full range that corresponds to all reactor operating powers. A quantitative comparison of the models are made using the root-mean-squared error (RMSE) and the minimum-descriptive-length (MDL) criteria. Furthermore, conventional back propagation learning-based neural and fuzzy models are also designed for comparing ELMs to classical artificial models. The advantages and disadvantages of the designed models are discussed. [ABSTRACT FROM AUTHOR]