Politecnico di Milano

Politecnico di Milano (Italy) founded in 1863 is the oldest of Milan’s universities and Italy’s largest school of Architecture, Design and Engineering and one of the best scientific-technological universities in the world according to the QS World University Rankings, classified 1st in Italy and 156th worldwide (Ranking 2018-2019).

The Department of Management, Economics and Industrial Engineering (DIG) of Politecnico di Milano was established in 1990. The Department’s main mission is to impact on society by creating and sharing knowledge at the intersection between engineering, management and economics through outstanding research, top quality education and serving the community. DIG research aims to produce excellent science though a tailored approach characterized by multi- disciplinarity, mastering of multiple methodologies and intense connections with practitioners and policymakers.

The Department is part of the School of Management of Politecnico di Milano, established in 2003 together with MIP Politecnico di Milano Graduate School of Business which focuses on post-experience education. The School is EQUIS and AMBA accredited and ins ranked by Financial Times and QS among the best European Business Schools. The School is member of PRME, Cladea, ACE and QTEM.

Role in the project:

Politecnico di Milano is primarily involved in WP4 and WP5. It will provide data-driven insights on the role of large-scale interventions to promote energy-efficient household behaviours within new societal trends identified at European level, and within the European policy needs for 2050 energy demand goals (Task 4.2) and improve the statistical and computational tools by which big and new data sources can inform energy demand models related to the built environment (Task 5.1).

Politecnico di Milano will cooperate with the other partners to draw insights from available pilot studies on demand-side policy interventions, as well as with modellers to advance and streamline the paradigm by which newly collected high-frequency datasets can effectively improve policy assessment of existing bottom-up energy demand models.