Efficiency of Using Artificial Neural Network for Short-Term Load Forecasting

Autor:	Alexander Rodygin, Valentina Lyubchenko, Svetlana Rodygina
Rok vydání:	2015
Předmět:	Artificial neural network Electrical load business.industry Computer science Load forecasting Activation function General Medicine Machine learning computer.software_genre Term (time) Electricity market Artificial intelligence business computer Physics::Atmospheric and Oceanic Physics
Zdroj:	Applied Mechanics and Materials. 792:312-316
ISSN:	1662-7482
DOI:	10.4028/www.scientific.net/amm.792.312
Popis:	Using artificial neural networks (ANN) for short-term load forecasting is an efficient method to get the best result. Considered problem of short-term load forecasting shows that the accuracy of short-term forecasting models and methods significantly influences on the further planning of operating conditions at the modern electricity market. The obtained error for short-term load forecasting using the neural network algorithm is 2.78%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3f3c4ac2dbb67df1e6e6ddc69aab577e https://doi.org/10.4028/www.scientific.net/amm.792.312 Zobrazit plný text záznamu