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Support Tool for Energy Efficiency pRogrammes in medical centres

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New software improves energy efficiency in EU hospitals

Medical centres are woefully inefficient. This new software predicts the best ways to change that.

Energy

Improving energy efficiency across the EU would be an effective way to lower greenhouse gas emissions, significantly helping towards reaching the reduction targets set by the European Commission. This means creating effective analytical and decision-assisting tools to see where the problems lie. Medical centres typically use large amounts of energy, and inefficiently. They must adhere to strict regulations – such as air quality – which take energy to meet. Hospitals often require the near-constant use of specialised technical machinery, too; each piece of equipment coming with a different electrical profile. “Energy consumption and pollution emissions can’t be significantly reduced at a European level without dramatically improving energy efficiency in hospitals,” says Mr Daniele Liberanome, Head of International and Special Projects at Zephyro in Milan, and STEER researcher. So the Horizon 2020-funded STEER project developed a method with which medical centres across the EU – as well as governments, utilities providers, auditors and internal management – could assess and monitor the energy usage across each facility. Energy consumption can therefore be measured, showing where easy-won efficiency gains could come from. This facilitates the creation of medium-to-long-term plans to reduce energy use. The prediction software is based on mathematical models, which identify and rank variables in medical centres that use considerable amounts of energy. This research, which was undertaken with the support of the Marie Skłodowska-Curie programme, was divided into five phases. First, data were collected directly from hospitals. Then Liberanome highlighted energy-related issues in hospitals and devised a model to describe them. This involved simulating environments within hospitals, and directly correlating with the previously harvested data. The models were tested and tweaked accordingly, and a sensitivity analysis was carried out. Finally, the team funnelled all of the knowledge it had acquired into creating a piece of predictive and analytical software. “We developed an open source web-based application: a software prototype that presents a scenario-based assessment and prediction tool,” says Liberanome. He hopes that in using the STEER model, hospitals – and eventually other energy-consuming institutions – will be able to create energy efficiency plans, compare efficiency levels between similar hospitals, plan energy-based investments, and adopt new, energy-efficient technologies. Hospitals should also be able to import ‘best practices’ from other similar buildings, homogenising and improving systems across the EU. Healing inefficiency STEER found numerous variables that contributed to poor energy consumption practice in the hospitals they tested the programme in. These include heating and cooling systems, lighting, overall energy system efficiency, and long working hours. There were a couple of hurdles to jump in the process, particularly as some hospitals lacked the necessary data the team needed to do their analysis. Using machine learning, however, the research was able to define the relative contribution of each variable to overall energy consumption. One important finding was showing how crucial it is to invest in building insulation, even if this requires higher investment and a longer return-on-investment (ROI). There are clear short and long-term benefits. In the short term, hospitals benefit from a more stable energy consumption profile and less overall consumption. As investments are carried out and new machinery becomes fully operational, results continue into the long term, compounded by awareness campaigns and the improved training of staff to use the new tools. Liberanome is proud of the work done: “The results are interesting, we overcame the difficulties that we had to face, we reached the targets and most importantly, we created a strong and cohesive team.” The project group intends to continue developing the prototype with the support of some other consortium members, while pushing new projects and ideas. “We are looking to create a wider and stronger collaboration,” Liberanome adds.

Keywords

STEER, energy efficiency, hospitals, greenhouse gas reductions, lighting, heating, cooling, predictive technology

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