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Data Science for Building Management Systems

Francesca Stobbione

Data Science for Building Management Systems.

Rel. Guido Perboli, Filippo Velardocchia. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Della Produzione Industriale E Dell'Innovazione Tecnologica, 2022


Data Science technologies such as Predictive Modeling, Scheduling, Optimization, Clustering, Statistical Process Control for the optimization of processes related to the consumption of utilities (electricity, gas and water) of a logistics plant. The Predictive analytics team support the Amazon climate pledge goal of achieving net-zero carbon emissions by 2040, though the development of analysis, use cases and models. The use cases produced by the PAn team as part of the Sustainability RME roadmap are intended to support areas of consumption of utilities that can be optimized without the need for extra capex or installations. The models included in the thesis lay the groundwork for a future Sustainability Analytics function, when data availability and quality are network-wide. The relevance of the efforts is to shed light on the existing situation and determine which data correction activities should be prioritised based on their potential benefits. In order to successfully benefit from the solutions created and to support the thesis presents there are specific data gathering and cleaning activities and requirements that needs to be implemented, in addition to an overview of the future status of RME sutainability projects.

Relators: Guido Perboli, Filippo Velardocchia
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 96
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Ingegneria Della Produzione Industriale E Dell'Innovazione Tecnologica
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Aziende collaboratrici: AMAZON EU SARL
URI: http://webthesis.biblio.polito.it/id/eprint/25254
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