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Project Description

Energy demand forecasting is the foundation for efficient Sector Coupling, and it is a fundamental business problem in the energy sector. High accuracy of energy demand forecasting is essential to realizing sector coupling solutions. Increasing the accuracy of a forecasting model is difficult and highly depends on the quality of training data, the selection of machine learning algorithms, the speed of training processes, and the evaluation metrics. The current State-of-the-Art methods are based on expert knowledge or prior experiences to select the data initially and data analysis using correlation coefficients or similar methods which are often labor-intensive and time-consuming.

Therefore, automating the data pipeline that can identify and select the most relevant data, and train the AI models will reduce time and labor costs dramatically.

This project aims to develop, test and deploy a data and machine learning pipeline that can automatically select relevant input data, best-fit machine learning algorithms and evaluation metrics for creating energy demand forecasting models and evaluating their performance.

The project will utilize the open-source OpenDataHub platform from Red Hat. The OpenDataHub platform can be installed locally and allows the developed pipeline to be deployed and tested on-premises. The OpenDataHub platform is designed to be easily scalable for future scenarios and does not require partners to develop any new software infrastructure to support the project, only task-specific extension modules related to the energy domain need to be developed and added to the platform.

This project is a continuation of the Lighthouse South phase one project “AI-based forecasting framework for sector coupling between electricity grids and district heating” and will integrate the experiences learned from the manual development of AI-based forecasting models into the creation of the automated data pipeline.

Project Summary