Optimising energy use with machine learning
22 January 2020
- Collaboration with Fetch.ai explores machine learning in energy systems
- Initial results from a 'virtual twin' reduced energy costs by up to 18 per cent
- It could optimise heat and power production and use across campus
- Model-free AI approach learns the optimal control policy from historical data
In September this year, the University of Warwick declared a Climate Emergency and set ambitious “net zero” goals for emissions.
Achieving this involves using less energy on the campus, and paying attention to energy used in heating, cooling and transport as well as the usual focus on electricity.
Considering the supply, storage and use of energy across multiple energy vectors rapidly becomes complex. Different existing control systems model separate energy vectors across different parts of the campus.
Artificial intelligence and machine learning are enabling a transactive energy approach where key parts of the energy system react and respond in ways not previously possible.
WBS Professors of Practice David Elmes and Mark Skilton have collaborated with Fetch.ai, one of the leading companies in the use of AI, to offer a decentralised connectivity platform that enables devices to connect directly with digital agents delivering autonomous solutions to complex tasks.
Development of the machine learning approach is led by Dr Yujian Ye, of Fetch.ai, in collaboration with Chris Conlan, a data science PhD student in the Warwick Institute for the Science of Cities, together with Joel Cardinal and Mark Jarvis of the University of Warwick Estates department.
Professor Elmes said: “We need innovative approaches to running multi-vectoral energy systems more efficiently. Warwick has run our campus as a smart, local energy system for a decade or more and reduced our emissions per unit of income by 46 per cent since 2006. But we need to do more to reach net zero.”
Professor Skilton said: “This approach uses multi-agent reinforcement learning to give a model-free, data driven approach where AI agents representing key energy assets across the campus learn the optimal control policy from real-world historical data.
"A virtual twin can then run alongside the real system to demonstrate the different energy management strategies that arise from the AI approach. We have started with the combined heat and power engines we have for self-generating energy on campus along with thermal storage and the demand for heat and electricity.
"That initial scope is a small part of the campus energy system overall and initial results suggest potential for a 13-18 per cent reduction in energy costs compared with existing control systems. But we now need to study what decisions the AI twin has made and how they can be brought across into the real world.”
Humayun Sheikh, co-founder and CEO at Fetch.ai, added: “Fetch.ai uses autonomous software agents to complete useful economic work in a wide range of different markets.
“Our collaboration with Warwick Business School shows that energy systems offer a great example of how our decentralised AI capability empowers efficient decision-making. Enabling autonomous agents to work this way unleashes the true potential for an internet of things.”
Two years’ worth of data across 16 sensors on the Warwick campus provided more than 200,000 data points for Fetch.ai. The energy management strategy of each asset was then optimised using an individual actor neural network.
Professor Elmes said: “This is only an initial step in applying machine learning to the more efficient and sustainable management of a smart local energy system. A model-free, data driven approach offers an exciting alternative for the greater complexity of energy systems that want to include heat, power and transport in a smart local energy system approach.”
David Elmes is part of the EPSRC-funded LoT-NET research programme into the next generation of low temperature heat networks. He also champions the smarter management of heating and cooling in the EnergyREV consortium, part of the UK Industrial Strategy Challenge Fund’s programme on Prospering from the Energy Revolution.