Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks
Journal:
arXiv
Published Date:
Dec 20, 2024
Abstract
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.
