Date: 20 May 2021
Speaker: Michael Spence
Title: AI for Bill Reduction
AI for Bill Reduction investigates the use of AI for the task of optimising energy usage in both commercial and domestic settings. The idea is to use reinforcement learning (RL), given the potential it has shown in solving complex optimisation problems. Businesses have several incentives to reduce energy consumption. Lower consumption means lower energy bills and saved money. This is especially relevant to high consumption sectors such as manufacturing and heavy industry, and given the fact that most of the world’s energy is still produced from fossil fuel sources, reduced consumption also helps to combat climate change and lower environmental pollution. In order to clearly show the potential of RL for energy optimisation, we developed a demonstrator application as part of this project. This application provides a simple simulation environment which allows for the comparison between RL and traditional energy optimization methods in a domestic heating and cooling scenario. This video explains the demonstrator application developed for this project and its main features and functionalities.
Online Recording: https://youtu.be/fT5jKywFKnw