RESA uses Cloud and Machine Learning to forecast energy demand

Challenges

Energy demand forecasting models tailored to businesses for better decision-making

RESA, a company specializing in energy networks, was facing significant challenges in accurately forecasting energy demand across its entire service area.

It relied on traditional methods that were time-consuming and lacked precision, resulting in inefficiencies in managing its energy network and distribution operations.

RESA realized that integrating a user-friendly forecasting web application based on machine learning (ML) algorithms could improve its operational and business decisions, enabling it to optimize its operations, reduce costs, and therefore improve customer satisfaction.

However, RESA lacked the internal expertise and resources needed to develop the sophisticated linear modeling algorithms required for accurate energy demand forecasting. It also recognized that incorporating socio-demographic and geographic data and scientific research models could further enhance its forecasting models. They therefore decided to seek help from Talan (previously 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

Talan (previsouly Micropole BeLux’s Finance Transformation & Performance team) proposed a solution that addressed these challenges. They developed a custom web application that incorporates ML algorithms based on RESA’s energy network data, socio-demographic data, geographic data, and scientific energy consumption models. The application enabled RESA to input real-time data on network energy load and produce accurate energy demand forecasts for different time horizons, with a high degree of precision.

The web application developed by Talan (previously Micropole BeLux’s Finance Transformation & Performance team) provided RESA with a comprehensive and user-friendly platform to access and analyze real-time energy demand forecasts. The application also integrated visualizations and dashboards that enabled RESA to better understand energy demand patterns, identify trends, and make informed decisions to optimize its operations.

Results

The development of an energy consumption prediction visualization application is very promising 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 insights into energy usage patterns.

Energy network companies can benefit from this technology by gaining valuable insights into energy usage patterns and demand, enabling them to manage energy distribution more efficiently and plan future infrastructure investments.

Overall, the development of predictive applications for energy consumption could be a game-changer for the energy sector, enabling the establishment of a more sustainable and efficient energy network.

Future research in this area could focus on exploring additional features and functionalities, as well as the potential for integrating renewable energy sources into the application.