Adaptive Bi-Directional LSTM Short-Term Load Forecasting with Improved Attention Mechanisms

Autor:	Kun Yu
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	load forecast clustering bi-directional LSTM attention mechanism pattern recognition Technology
Zdroj:	Energies, Vol 17, Iss 15, p 3709 (2024)
Druh dokumentu:	article
ISSN:	1996-1073
DOI:	10.3390/en17153709
Popis:	Special load customers such as electric vehicles are emerging in modern power systems. They lead to a higher penetration of special load patterns, raising difficulty for short-term load forecasting (STLF). We propose a hierarchical STLF framework to improve load forecasting accuracy. An improved adaptive K-means clustering algorithm is designed for load pattern recognition and avoiding local sub-optimal clustering centroids. We also design bi-directional long-short-term memory neural networks with an attention mechanism to filter important load information and perform load forecasting for each recognized load pattern. The numerical results on the public load dataset show that our proposed method effectively forecasts the residential load with a high accuracy.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/f841ff0bce1d40efba2ea3832789652b Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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