Energy Consumption Modelling and Forecasting for Commercial Industrial Manufacturing Applications

Professor Michael Short,
School of Computing, Engineering and Technology Teesside University, Middlesbrough
TS1 3BA, UK

Abstract: In the United Kingdom, industry accounts for roughly a quarter of greenhouse gas emissions. The UK Government has set ambitious net zero targets committed to the decarbonisation of heavy industry, and the Industrial Clusters mission aims to establish the world's first net-zero carbon industrial cluster by 2040. To reduce the energy costs and carbon footprint of industry, one of the most effective solutions is the use of digital tools enabling businesses to monitor and visualize their energy consumption in real-time. Due to recent advancements in industrial digitalization, many industrial sites already generate data, including energy monitoring data, with varying degrees of digital maturity. However, a major challenge with this data is a lack of commercial tools for modelling, predicting, and visualizing industrial manufacturing energy data for the purposes of efficiency improvement and emissions reduction. This paper describes efforts in a recent funded project to develop a prototype flexible, industrial energy efficiency and visualization profiling Toolbox (I-CAT). The toolbox embeds energy analytics and Machine Learning (ML) capabilities into an existing commercial SCADA platform for industrial manufacturing operations. This approach allows the creation of an energy Digital Twin. The paper describes requirements of the toolbox, and experimental analysis of the toolbox in a case study, an operational sawmill in Carlisle, UK. Data-driven modelling allows the creation of a predictive model of energy consumption of the facility from a forecasted production schedule. Mean average modelling errors of less than 10% were obtained. The paper concludes by highlighting areas of future development work.

Brief Biography of the Speaker: Michael Short is professor of control engineering and systems informatics at Teesside University in the UK and leads the multidisciplinary Centre for Sustainable Engineering. He holds a BEng degree in electronic and electrical engineering (1999, Sunderland) and a PhD degree in real-time robot control (2003, Sunderland). Michael’s research interests encompass aspects of applied control engineering and systems informatics applied to smart energy systems and robotics. He has authored over 160 reviewed publications in international conferences and journals, has over 1400 citations and has won six best paper awards. He currently has an h-index of 23 and an i-10 index of 41. He has supervised six PhD completions and is investigator on numerous completed and ongoing funded research projects. He is an associate editor for the International Journal of Energies, a full member of the IET, a member of the IEEE Industrial Electronics Society Technical Committee on Factory Automation and a fellow of the HEA.