Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks

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I whakaputaina i:	arXiv.org (Dec 20, 2024), p. n/a
Kaituhi matua:	Nguyen, Bang
Ētahi atu kaituhi:	Panwar, Mayank, Hovsapian, Rob, Agalgaonkar, Yashodhan
I whakaputaina:	Cornell University Library, arXiv.org
Ngā marau:	Recurrent neural networks Energy management Memory devices Residential energy Electrical loads Internet of Things Smart grid Modelling Forecasting
Urunga tuihono:	Citation/Abstract Full text outside of ProQuest
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Whakaahuatanga
Whakarāpopotonga:	Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT devices in smart grids. This paper investigates the IoT device modeling of a thermostatic load and implements the recurrent neural networks model for short-term load forecasting in this IoT-based thermostatic load. The recurrent neural network structure is leveraged to build a load forecasting model on temporal correlation. The temporal recurrent neural network layers including long short-term memory cells are employed to learn the data from both the simulation platform and New South Wales residential datasets. The simulation results are provided for demonstration.
ISSN:	2331-8422
Puna:	Engineering Database