Publicação
Optimising Energy Analytics: A Dimensional Modelling Approach for Enhanced Decision-Making
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Jardim, João Bruno Morais de Sousa | |
| dc.contributor.author | Aires, Vitor André Jóia | |
| dc.date.accessioned | 2025-11-07T11:48:09Z | |
| dc.date.available | 2025-11-07T11:48:09Z | |
| dc.date.issued | 2025-10-27 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence | pt_PT |
| dc.description.abstract | The accelerating digital transformation of the energy sector presents new opportunities and challenges in achieving sustainable, efficient, and resilient energy systems. This thesis addresses the integration of consumption analytics, grid infrastructure, and electric mobility by designing a scalable data architecture rooted in dimensional modelling and implemented using Microsoft Fabric. Drawing on large-scale datasets from the Portuguese electricity distribution operator E-REDES, the study adopts a Medallion architecture framework— structuring data flows through Bronze, Silver, and Gold layers—to deliver actionable insights via Power BI dashboards. Key analytical domains include hourly and regional energy consumption, electric vehicle infrastructure impacts, service continuity (measured via SAIDI and SAIFI indices), and renewable energy adoption. By applying the Kimball Data Warehouse Lifecycle, the work ensures that the dimensional star schema reflects both technical robustness and stakeholder relevance, leveraging conformed dimensions and surrogate keys for analytical consistency. The thesis demonstrates how integrated data models can support demand forecasting, service reliability benchmarking, and policy alignment. Moreover, it highlights the potential of unified data platforms in operationalizing real-time energy management while exposing gaps in data granularity, public infrastructure availability, and behavioural modelling. Ultimately, this research contributes to the field of data-driven energy systems by offering a reusable architectural blueprint that is both technically scalable and policy-relevant. | pt_PT |
| dc.identifier.tid | 204075319 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190265 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Energy Analytics | pt_PT |
| dc.subject | Dimensional Modelling | pt_PT |
| dc.subject | Data Warehouse | pt_PT |
| dc.subject | Microsoft Fabric | pt_PT |
| dc.subject | Electric Mobility | pt_PT |
| dc.subject | Smart Grids | pt_PT |
| dc.subject | Renewable Energy | pt_PT |
| dc.subject | Service Continuity | pt_PT |
| dc.subject | Business Intelligence | pt_PT |
| dc.subject | Data Architecture | pt_PT |
| dc.subject | Power BI | pt_PT |
| dc.subject | SAIDI | pt_PT |
| dc.subject | SAIFI | pt_PT |
| dc.subject | Medallion Architecture | pt_PT |
| dc.subject | Kimball Methodology | pt_PT |
| dc.subject | Data Integration | pt_PT |
| dc.subject | Geographic Energy Analysis | pt_PT |
| dc.subject | Electric Vehicle Infrastructure | pt_PT |
| dc.subject | Load Forecasting | pt_PT |
| dc.subject | Grid Resilience | pt_PT |
| dc.subject | SDG 7 - Affordable and clean energy | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.subject | SDG 11 - Sustainable cities and communities | pt_PT |
| dc.subject | SDG 12 - Responsible production and consumption | pt_PT |
| dc.subject | SDG 13 - Climate action | pt_PT |
| dc.title | Optimising Energy Analytics: A Dimensional Modelling Approach for Enhanced Decision-Making | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Gestão do Conhecimento e Inteligência de Negócio | pt_PT |