Deep Learning with Dipper Throated Optimization Algorithm for Energy Consumption Forecasting in Smart Households

One of the relevant factors in smart energy management is the ability to predict the consumption of energy in smart households and use the resulting data for planning and operating energy generation. For the utility to save money on energy generation, it must be able to forecast electrical demands a...

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Veröffentlicht in:	Energies (Basel) Jg. 15; H. 23; S. 9125
Hauptverfasser:	Abdelhamid, Abdelaziz, El-Kenawy, El-Sayed, Alrowais, Fadwa, Ibrahim, Abdelhameed, Khodadadi, Nima, Lim, Wei, Alruwais, Nuha, Khafaga, Doaa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.12.2022
Schlagworte:	Algorithms Alternative energy sources Architecture Artificial intelligence Deep learning dipper throated optimization Energy consumption Energy efficiency Energy industry Energy management Energy resources Forecasting Forecasting techniques Households Load long short-term memory Machine learning Mathematical optimization meta-heuristic optimization Neural networks Optimization techniques Renewable resources smart household Time series Saudi Arabia
ISSN:	1996-1073, 1996-1073
Online-Zugang:	Volltext
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