vantago GPT (2019)
Web app for calculating a gas forecast temperature for optimized SLP allocation.
Dashboard with app tiles and KPIs (screenshot web app)
Network account deviations by month with daily balances and limit overruns (screenshot web application).
Graphical and tabular evaluation of feed-in and feed-out data of arbitrary time periods (screenshot web application)
Gas price maintenance (screenshot web application)
Logbook with all system events and possibility to download the gas forecast temperature as CSV file (screenshot web application)
Client:
In collaboration with:
1 Product Owner, 2 Developers
Services provided:
UX/UI design, Support for the implementation of the frontend
Tools used:
HTML/CSS, Bootstrap Framework, jQuery
Problem/Requirements:
Gas network operators use standard load profiles (SLPs) to forecast next-day gas demand, aiding in effective gas supply planning. SLPs provide a reference model for estimating consumption patterns and ensuring reliable gas supply.
The outside temperature is a significant factor in the gas demand forecast, with colder days requiring more gas and warmer days requiring less. Gas network operators consider the temperature variations to accurately anticipate and adjust the gas supply accordingly, ensuring efficient distribution and utilization of resources.
vantago has developed a machine learning-based method to calculate an optimized temperature, leading to more accurate predictions in gas demand forecasting. By leveraging advanced algorithms and data analysis techniques, this approach enhances the accuracy of temperature-based predictions, allowing for improved forecasting of gas demand. The utilization of machine learning enables vantago to optimize the forecasting process and provide more precise insights for efficient gas supply management.
Solution:
Concise web-based application with a user-friendly dashboard that presents essential key figures for quick and easy monitoring and analysis.
The web-based application provides detailed evaluations in the form of diagrams, showcasing data for different time periods. These visual representations offer clear before-and-after comparisons, enabling users to easily analyze and understand the impact of changes or developments over time. The diagrams provide comprehensive insights, supporting data-driven decision-making and facilitating a deeper understanding of performance trends and improvements.
Possibility to maintain the gas price
Logbook with all system events for status check and error diagnosis