Publicação
Energy Consumption Distribution in Portugal: A Business Intelligence Solution
| Resumo: | This thesis explores the distribution and temporal patterns of energy consumption in Portugal through a comprehensive Business Intelligence (BI) solution. This study provides insights into monthly and hourly energy consumption behaviors on a geographical scale, which contributes to improving energy efficiency policies. Three datasets from the open data platform of ERedes, a Portuguese entity responsible for electricity distribution, were used in this analysis: monthly energy consumption, hourly energy consumption, and total consumption point. The data covers multiple years, enabling an in-depth analysis of consumption patterns. Using the Design Science Research Methodology (DSRM), this research develops a BI conceptual model for energy consumption analysis. The methodology is composed of five steps: identification of the problem and research objectives; design and development of a BI conceptual model; evaluation through business questions; visualization of results; and feedback. The resulting BI model is visualized through a dynamic dashboard in Power BI, providing an interactive platform for data exploration and decision-making support. The analysis identifies notable seasonal peaks in energy consumption during January, July, and March, influenced by weather conditions and societal behaviors. Hourly data shows that energy consumption peaks in the morning and evening, with significant reductions during the early morning and night. This detailed examination of consumption patterns provides valuable insights for policymakers and energy managers, enabling the design of targeted energy efficiency measures. In summary, the findings highlight the potential of BI tools to support sustainable urban development and energy management strategies, which can lead to reduced energy consumption and improved environmental sustainability in Portugal. Future efforts will refine the BI model, incorporate real-time data, and broaden the analysis to include additional variables. |
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| Autores principais: | Silva, Telma Machado Bota da |
| Assunto: | Business Intelligence Data Visualization Energy Consumption Data Analysis Smart Cities SDG 11 - Sustainable cities and communities SDG 12 - Responsible production and consumption |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | This thesis explores the distribution and temporal patterns of energy consumption in Portugal through a comprehensive Business Intelligence (BI) solution. This study provides insights into monthly and hourly energy consumption behaviors on a geographical scale, which contributes to improving energy efficiency policies. Three datasets from the open data platform of ERedes, a Portuguese entity responsible for electricity distribution, were used in this analysis: monthly energy consumption, hourly energy consumption, and total consumption point. The data covers multiple years, enabling an in-depth analysis of consumption patterns. Using the Design Science Research Methodology (DSRM), this research develops a BI conceptual model for energy consumption analysis. The methodology is composed of five steps: identification of the problem and research objectives; design and development of a BI conceptual model; evaluation through business questions; visualization of results; and feedback. The resulting BI model is visualized through a dynamic dashboard in Power BI, providing an interactive platform for data exploration and decision-making support. The analysis identifies notable seasonal peaks in energy consumption during January, July, and March, influenced by weather conditions and societal behaviors. Hourly data shows that energy consumption peaks in the morning and evening, with significant reductions during the early morning and night. This detailed examination of consumption patterns provides valuable insights for policymakers and energy managers, enabling the design of targeted energy efficiency measures. In summary, the findings highlight the potential of BI tools to support sustainable urban development and energy management strategies, which can lead to reduced energy consumption and improved environmental sustainability in Portugal. Future efforts will refine the BI model, incorporate real-time data, and broaden the analysis to include additional variables. |
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