Short-term load forecasting using artificial neural network
| Autor: | Pawar Vidya, Giriyappa Ankaliki Shekhappa, S. Sureban Manjula |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | i-manager's Journal on Power Systems Engineering. 10:13 |
| ISSN: | 2322-0376 2321-7499 |
| DOI: | 10.26634/jps.10.1.18841 |
| Popis: | One of the major research topics in electrical engineering in recent years is load prediction. Short-term load forecasting is necessary for the design, operation, and management of the power system. It is used, among others, by utilities, system operators, electricity producers, and suppliers. Artificial Neural Networks (ANN) have been used for short-term load prediction. The work has been completed to ensure day-to-day operations. Here, the proposed neural networks were trained and tested using newly available data from Hubli Electricity Supply Company Limited (HESCOM). This paper presents a method for predicting the load of a power system based on a Neural Network (NN). Matrix Laboratory (MATLAB) software is used to create training and test simulations. The error was defined as Mean Absolute Percentage Error (MAPE). |
| Databáze: | OpenAIRE |
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