Ieee papers on short term load forecasting

short term load forecasting wiki

The building block of the forecasters is a multilayer perceptron trained with the error backpropagation learning rule. The accuracy of such forecasts has significant economic impact for the utility.

The proposed load forecasting approach is applied to multiple data sets and the results obtained are compared to published results.

short term load forecasting in power system

An adaptive scheme is employed to adjust the ANN weights during online forecasting. This paper describes a load forecasting system known as ANNSTLF artificial neural-network short-term load forecaster which has received wide acceptance by the electric utility industry and presently is being used by 32 utilities across the USA and Canada.

short term load forecasting in power system

The results of testing the system on data from ten different utilities are reported. A key component of the daily operation and planning activities of an electric utility is short-term load forecasting, i.

The forecasting models are site independent and only the number of hidden layer nodes of ANN's need to be adjusted for a new database.

Load forecasting methods

The proposed load forecasting approach is applied to multiple data sets and the results obtained are compared to published results. An adaptive scheme is employed to adjust the ANN weights during online forecasting. The proposed approach utilizes the historical hourly load data for accurate estimation of loads. The comparisons and subsequent discussions show the efficiency of the proposed method and its superiority over other load forecasting techniques. The forecasting models are site independent and only the number of hidden layer nodes of ANN's need to be adjusted for a new database. Author information: 1 Dept. Resources and Help Short term load forecasting using ANN technique Abstract: Load forecasting is a very important tool for energy suppliers and other participants in electric energy generation, transmission, and distribution markets. Many mathematical methods were proposed for short and long term load forecasting. This paper presents an approach for short-term load forecasting using the artificial neural network technique.
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A Methodology for Short