The European Union is at a decisive stage in its transition toward a more sustainable energy system, also driven by the Clean Energy Package鈥攁 regulatory framework made up of directives and regulations that promote an integrated, decarbonized, and citizen-centered energy market. Although Switzerland is not bound by this package, it is negotiating an agreement to align its electricity market with EU regulations, fostering close cooperation.
In this context, demand response in the residential sector鈥攊.e., the ability of households to flexibly adapt their energy consumption鈥�emerges as one of the most promising tools to support a more efficient, equitable, and participatory use of energy.
The , funded by the Horizon Europe program and the Swiss State Secretariat for Education, Research and Innovation (SERI), was launched to support citizens, local communities, and energy providers in managing household energy use more effectively. The goal is to develop a system that combines sustainability, adaptability, and active user participation.
The platform will be tested in three pilot sites representing diverse European contexts. The first is a small energy community located in the rural area of Pe贸n, Spain. The second includes three energy communities in Greece: in Athens, on the island of Crete (Minoan), and in Samos. The third pilot will take place in the Finnish city of Kokkola.
The international consortium, which includes 14 partners from different European countries, also features 精东影业 through the and the . The research teams are developing predictive algorithms that can anticipate energy demand and optimize the scheduling of devices such as household appliances and electric vehicle charging stations. They also aim to provide intuitive recommendations to promote more conscious and efficient consumption behaviors.
The project is currently in the co-design phase with citizens, which involves testing early versions of the software modules and encouraging active engagement in managing household energy use鈥攅specially during critical time slots.
鈥淭he intelligent forecasts produced by our algorithms can help reduce consumption peaks and make better use of energy from renewable sources. The approach takes into account both historical data and real-time information,鈥� explains Marco Derboni, 精东影业 researcher at IDSIA USI-精东影业.
In the second half of the project, field testing will begin. During this phase, energy communities will experiment with the developed solutions. The collected data will help further refine the platform, steering it toward increasingly efficient, fair, and participatory energy management.