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

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.

I. Introduction

The electric grid modernization requires more cutting-edge technologies in information than just traditional generation, transmission, and distribution infrastructures. For better monitoring, control, and management of the grid, communication, and control systems play crucial roles [1]. Internet of Things (IoT) technology, which is an emerging technology, enables the integration of information and communication infrastructure to monitoring and controlling devices [2]. IoT enhances the available and reliable data communication for intelligently controlling and managing the grid. IoT devices can also benefit the end-user by providing personalized energy services [3]. Generally, IoT can be integrated into smart meters, electric vehicles, solar photovoltaic panels, and battery energy storage systems, to name a few. Hence, these devices can be involved in supporting the grid.