State of Energy Prediction in Renewable Energy-driven Mobile Edge Computing using CNN-LSTM Networks

Abstract

Renewable energy (RE) is a promising solution to save grid power in mobile edge computing (MEC) systems and thus reducing the carbon footprints. However, to effectively operate the RE-based MEC system, a method for predicting the state of energy (SoE) in the battery is essential, not only to prevent the battery from over-charging or over-discharging, but also allowing the MEC applications to adjust their loads in advance based on the energy availability. In this work, we consider RE-powered MEC systems at the Road-side Unit (RSU) and focus on predicting its battery's SoE by using machine learning technique. We developed a real-world RE-powered RSU testbed consisting of edge computing devices, small cell base station, and solar as well as wind power generators. By operating RE-powered RSU for serving real-world computation task offloading demands, we collect the corresponding data sequences of battery's SoE and other observable parameters of the MEC systems that impact the SoE. Using a variant of Long Short-term Memory (LSTM) model with additional convolutional layers, we form a CNN-LSTM model which can predict the SoE accurately with very low prediction error. Our results show that CNN-LSTM outperforms other Recurrent Neural Networks (RNN) based models for predicting intra-hour and hour-ahead SoE.