Deep Clustering Algorithm for Load Profile Business Intelligence Dashboard for Consumer and Utility Management - Veloso, Artur Felipe Da Silva; Reis, José Valdemir Dos; Vasconcelos, Fillipe Matos; Silveira, Jocines De La Flora; Abreu, Pedro; Sarmento, Geraldo; Silva, Thiago Allisson; Rabêlo, Ricardo Lira (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI), 2024)
Context: The implementation and optimization of a Smart Grid (SG), incorporating Smart Meters (SMs) for the collection of energy consumption data, generation and grouping of Load Profiles (LPs), and the use of Business Intelligence (BI) tools for data analysis and visualization, offer significant opportunities for energy efficiency and the management of energy consumption. However, challenges related to non-data-driven decision-making are still prevalent. Problem: Difficulties in efficient energy management and uninformed decision-making are identified, impacting on the efficiency and quality of the electricity supply, as well as the costs for the end consumer. Proposed Solution: This work focused on the implementation of BI dashboards for both the utility and end consumers. It used detailed data and LPs grouped by deep clustering algorithms based on time series, with a view to strategic data analysis and visualization. IS Theory: Integrating SMs, LP analysis, and BI tools strategically informs energy management and decision-making for both the supply company and consumers.Method: Time series techniques were used to generate LPs and clustering algorithms to group them. BI dashboards were implemented, providing detailed views for consumers and the EPC. Results: The results showed empowerment for informed and integrated decision-making, cost savings, environmental contributions and a significant increase in energy efficiency. Contributions and Impact on IS: This work has contributed to efficient energy management, the integration of advanced data analysis techniques and the development of customized BI dashboards. The study demonstrated a positive impact on energy sustainability, offering a basis for the effective integration of SMs, LP analysis and BI tools in energy management.