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Information Retrieval from PDF of companies, calculation of the " Intensity" metric to assess emission.

Niccolo' Revel Garrone

Information Retrieval from PDF of companies, calculation of the " Intensity" metric to assess emission.

Rel. Paolo Garza. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2023

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Abstract:

This project proposes the development of an automated system for extracting data from tables of energy companies in order to calculate the intensity metric and evaluate companies in a standardized manner. The main objective is to provide an effective methodology for assessing energy efficiency and sustainability of energy companies, enabling homogeneous comparisons among them. In the initial phase of the project, the focus will be on extracting data from company tables. These tables may contain crucial information such as sales figures, emission values, and other relevant metrics. The system will employ advanced data extraction techniques, utilizing algorithms and machine learning models to accurately identify and extract the required data from diverse table formats and structures. Once the data has been successfully extracted, the next step is to calculate the intensity metric. The metric will be designed to quantify the relationship between energy consumption and sales for each company. By dividing the emissions by the sales figures, the intensity metric will provide a standardized measure of the energy efficiency of the company’s operations. This calculation will enable a fair and comparable assessment of companies’ sustainability performance. The final objective of the project is to establish a standardized evaluation framework for energy companies based on the intensity metric. The calculated metrics will be compared across different companies within the industry, allowing for benchmarking and identification of leaders in energy efficiency. This standardized evaluation will facilitate decision-making processes for stakeholders, including investors, regulators, and consumers, by providing them with an objective and transparent method to assess the sustainability performance of energy companies.

Relators: Paolo Garza
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 66
Subjects:
Corso di laurea: Corso di laurea magistrale in Data Science And Engineering
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Ente in cotutela: UNIVERSIDAD POLITECNICA DE MADRID - ETS DE INGENIEROS INFORMATICOS (SPAGNA)
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/28645
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