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

Guardado en:
Publicado en:arXiv.org (Dec 20, 2024), p. n/a
Autor principal: Nguyen, Bang
Otros Autores: Panwar, Mayank, Hovsapian, Rob, Agalgaonkar, Yashodhan
Publicado:
Cornell University Library, arXiv.org
Materias:
Recurrent neural networks
Energy management
Memory devices
Residential energy
Electrical loads
Internet of Things
Smart grid
Modelling
Forecasting
Acceso en línea:Citation/Abstract
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Resumen: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
Fuente:Engineering Database