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This paper presents an adaptive-network-based fuzzy inference system (ANFIS) for long-term natural Electric consumption prediction. Six models are proposed to forecast annual Electric demand. 104 ANFIS have been constructed and tested in order to finding best ANFIS for Electric consumption. The proposed models consist of input variables such as Gross Domestic Product (GDP) and Population (POP). All...
Accurate short term load forecasting (STLF) is a prerequisite for proper generation scheduling and reliable operation of power utilities. Conventional methods of STLF, suffer from the disadvantages such as lack of ability to accurately model the weather parameters affecting the load, lack of robustness for representing weekends and public holidays and of being computation intensive. Application of...
The STLF algorithms belong to the set of methodologies which aim to furnish more effectiveness in planning, operation and conduction in electric energy systems. Actions like, maintenance issues, network management, and eventual power purchase decisions within liberalized electricity markets require, among others, reliable next-hour load forecasts. Regressive methods are widely used. Artificial neural...
Load forecasting is vitally important for the electric industry in the deregulated economy. Short-term load forecasting (STLF) has always been a very important issue in power system planning and operation. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. However, forecasting electricity load is difficult because...
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