Short-Term Ambient Temperature Forecasting for Smart Heaters

Abstract

Maintaining Cloud data centers is a worrying challenge in terms of energy efficiency. This challenge leads to solutions such as deploying Edge nodes that operate inside buildings without massive cooling systems. Edge nodes can act as smart heaters by recycling their consumed energy to heat these buildings. We propose a novel technique to perform temperature forecasting for Edge Computing smart heater environments. Our approach uses time series algorithms to exploit historical air temperature data with smart heaters' power consumption and heat-sink temperatures to create models to predict short-term ambient temperatures. We implemented our approach on top of Facebook's Prophet time series forecasting framework, and we used the real-time logs from Qarnot Computing as a usecase of a smart heater Edge platform. Our best trained model yields ambient temperature forecasts with less than 2.66% Mean Absolute Percentage Error showing the feasibility of near realtime forecasting.