Short-Term Load Forecasting for Electrical Power Distribution Systems Using Enhanced Deep Neural Networks

The rationale for using enhanced Deep Neural Networks (DNNs) in the power distribution system for short-term load forecasting (STLF) originates from a thorough analysis of current trends, the emergence of the state-of-the-art use cases and approaches. STLF plays a crucial role in economic load dispa...

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Veröffentlicht in:	IEEE access Jg. 12; S. 186856 - 186871
Hauptverfasser:	Tsegaye, Shewit, Padmanaban, Sanjeevikumar, Tjernberg, Lina Bertling, Fante, Kinde Anlay
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Convolutional neural networks Data models Deep neural networks efficient and parallel genetic algorithm Electric power distribution Electric utilities EPGA enhanced LSTM Error analysis Forecasting Genetic algorithms load forecast Load forecasting Load history Load modeling Load shedding Long short term memory Neural networks Pattern analysis Peak load Power dispatch Predictive models Renewable energy sources Risk management Root-mean-square errors short-term load forecast Unit commitment Wind direction Wind speed
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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