In the paper the problem of the application of neural predictors for Short-Term Load Forecasting (STLF) for energy transactions planning in utility is presented. Several aspects of this topic are discussed, including identification of different load patterns for holidays and customer profiles, estimation of prediction intervals and optimal size of the order, according to the financial condition...

Gas demand possesses dual property of growing and seasonal fluctuation simultaneously, it makes gas demand variation possess complex nonlinear character. From previous studies know single model for nonlinear problem can’t get good results but accurately gas forecast were essential part of an efficient gas system planning and operation. In recent years, lots of scholar put forward combination mo...

the ability of artificial neural network (ann) for estimating the natural gas demand load for the next day and month of the populated cities has shown to be a real  concern. as the most applicable network, the ann with multi-layer back propagation perceptrons is used to approximate functions. throughout the current work, the daily effective temperature is determined, and then the weather data w...

This paper presents the application of interval type-2 fuzzy inference systems (IT2FIS) in short term load forecasting (STLF) on special days. This is a continuation work of application interval type-2 fuzzy systems (IT2FSs) using Karnik Mendel algorithm. Special days here mean local Balinese holidays such as national and local culture-based public holidays, consecutive holidays, and days prece...

This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as the learning samples in the forecasting process and selected by a fuzzy clustering technique acc...