Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) ...

The electric power load forecasting is critical for stable electric power system supply. In this paper, a seasonal ARIMA model was used to effectively forecast power load data characterized using periodicity. A numerical example reveals that the seasonal ARIMA model effectively forecast periodic power load.

Review and classification of electric load forecasting (LF) techniques based on artificial neuralnetworks (ANN) is presented. A basic ANNs architectures used in LF reviewed. A wide range of ANNoriented applications for forecasting are given in the literature. These are classified into five groups:(1) ANNs in short-term LF, (2) ANNs in mid-term LF, (3) ANNs in long-term LF, (4) Hybrid ANNs inLF,...

transmission network expansion planning (tnep) is an important component of power system planning. itdetermines the characteristics and performance of the future electric power network and influences the powersystem operation directly. different methods have been proposed for the solution of the static transmissionnetwork expansion planning (stnep) problem till now. but in all of them, stnep pr...

Practical application of thermionic space nuclear power systems necessitates the investigation of such systems responses to changes in operation and design parameters as well as load-following characteristics. The principle building block in these systems is the thermionic fuel element (TFE). A Thermionic Transient Analysis Model (TUAM) is developed to simulates transient and steady-state opera...