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Customized Cloud web application visualizing ML models for energy demand forecasting.
In order to make the most of the transformation of its data, the energy network operator RESA called on Micropole's expertise to develop a custom-made ML web application to visualize energy demand forecasts.
Context
Micropole has built on the recently implemented cloud infrastructure (AWS-SAP) a customized web application presenting an energy demand forecasting model adapted to the needs of companies, based on Machine Learning algorithms using data from the energy grid, socio-demographic and cartographic data and energy consumption models based on scientific research.
Challenges
Energy demand forecasting models adapted to businesses for better decision-making
RESA, a company specializing in energy networks, was facing significant difficulties in accurately forecasting energy demand throughout its service area.
It relied on traditional methods that were time-consuming and lacked accuracy, resulting in inefficiencies in its energy network management and distribution operations.
RESA realized that integrating a user-friendly web-based forecasting application based on machine learning (ML) algorithms could improve its operational and business decisions, allowing it to optimize its operations, reduce costs, and therefore improve customer satisfaction.
However, RESA lacked the internal expertise and resources to develop the sophisticated linear modeling algorithms required for accurate energy demand forecasting. It also recognized that incorporating sociodemographic and mapping data and scientific research models could further improve its forecasting models. So they decided to enlist the help of Micropole BeLux's Finance Transformation & Performance team, known for its expertise in cloud, ML and data analytics.
Methods and Solutions
Building a custom web application visualising ML energy demand forecasting model
Micropole BeLux's Finance Transformation & Performance team proposed a solution that met these challenges. They developed a custom web application that incorporated ML algorithms based on RESA's energy network data, socio-demographic data, mapping data and scientific energy consumption models. The application enabled RESA to capture real-time energy load data from the grid and produce accurate energy demand forecasts for different time horizons with a high degree of accuracy.
The web application developed by Micropole BeLux's Finance Transformation & Performance team provided RESA with a comprehensive and user-friendly platform to access and analyze energy demand forecasts in real time. The application also integrated visualizations and dashboards that allowed RESA to better understand energy demand patterns, identify trends and make informed decisions to optimize its operations.
Results
The development of an energy prediction visualization application holds great promise for energy network companies seeking to improve energy efficiency and sustainability. A data-centric strategy, based on modern technologies and machine learning algorithms, enables user-friendly access to a customized application, anticipates future costs, and provides real-time information on energy usage patterns.
Energy network companies can benefit from this technology by gaining valuable information about energy usage patterns and demand, allowing them to more effectively manage energy distribution and plan future infrastructure investments.
Overall, the development of predictive applications for energy consumption could be a game changer for the energy sector, enabling a more sustainable and efficient energy grid. Future research in this area could focus on exploring additional features and functionality, as well as the potential for integrating renewable energy sources into the application.
The Project
Make smarter, data-driven decisions based on ML predictions
Improved forecast accuracy
Improved data integration and governance
Business friendly interface
Scalability and flexibility
By combining cloud technologies (AWS, Azure, GCP) and SAP,
we weave an essential link between Business Processes and the world of data, laying the foundations for a digital future that is both innovative and efficient.
Thanks to refined forecasts, planning and resource allocation have been optimized, resulting in significant savings and better anticipation of energy fluctuations for rapid, effective reaction to shortages or surpluses.
Hyperscaler Manager
Technology solutions
