Our challenge

We address the challenges of capacity shortages in the electricity/energy grids infrastructure, uneven electricity production from renewable sources such as wind and solar, district heating distribution and the need for new solutions for energy storage in the new industrialization.

By establishing an expanded testbed in the form of the “ACE Sandbox” in collaboration with ACE, an innovation environment is created for the smart energy system of the future, where we collect and analyze data from smart electricity meters, BMS systems and local energy suppliers with AI/ML methods to optimize energy use, balance local networks and manage power consumption efficiently.

The current situation

We have a lack of systems/products/services that enable optimization of energy use in society. Large property portfolios have an unnecessarily high energy consumption, there are also no good tools to balance the consumption between properties, which leads to the power requirement being higher than it could be with efficient tools.

What do we want to achieve?

The project aims to develop innovations to analyse the energy demand of buildings under different circumstances by using historical data taking into account weather conditions, building information and the use of the building. This data is used to predict future energy needs in buildings, to balance energy needs between buildings, and to reduce total energy consumption.

  • Energy optimization

    We optimize energy consumption in large property portfolios by including data sets on construction, analyzing energy usage patterns, activities in the buildings, microclimate, etc. With the help of this combination of data sets that are not included in today’s property controls, more informed decisions about energy measures can be made.

  • Cyber security

    Real estate systems can be attractive targets for attackers, which is why we are also working to build cybersecurity into our systems.

  • Machine learning

    We work with machine learning to automatically recognize what are normal patterns in data from the properties, and what are deviations. To train our models faster, we use transfer learning, i.e. we use already trained models from similar properties and supplement the training data with a smaller amount from the current property.

  • Augmented reality and 3D visualization

    We use augmented reality and 3D visualization to inform energy technicians about the condition of the properties, and suggest measures.

Goal

The project’s main goal is increased innovation capacity through research collaboration. We address digital service development for sustainable/smart societies with one of our major societal challenges, reducing energy use. We base the work on the previous SSiO project, where we developed a digital platform for services with the Internet of Things. In this project, we apply the technology for increased innovation capacity within smart properties, more specifically reduced energy use in large property portfolios through increased understanding of how energy is used. Various data sets are analysed, where we use machine learning algorithms/methods to create energy profiles of properties.

The project will be a support system where we build up an R&D collaboration between academia, property owners, energy companies and companies that can exploit the project’s results and deliver solutions. After the project, the R&D collaboration continues as a sustainable ecosystem that lives on under its own power.

Target groups

The project’s target group is companies that develop service innovations for smart communities and municipalities as owners of needs to address one of our current biggest societal challenges regarding energy use. Our results will also promote other actors with large property holdings regarding services for energy efficiency/savings. Primarily, properties in Skellefteå and Piteå municipalities are included, but we will take in needs from, and spread the results to other municipalities in Norr- and Västerbotten. We will also make results available to the rest of Sweden, as similar technologies are used in other municipalities.