Extensive academic literature covers the topic of power and natural gas demand forecasting. Years of study and hundreds of students proposed many forecasting approaches, providing a solid foundation for practitioners.

Unfortunately, constraints imposed by real-world scenarios often prevent a straightforward application of existing contributions. For instance, reliable demand data may be published with a delay of weeks or even months, thus compromising the application of many short-term models, and forecasts must be used for weather features, impacting the overall performance.

Using case studies from real projects, we point out the most important gaps between requirements from the industry and academic works and propose solutions to mitigate the issues. We report here three examples.

Concerning gas demand forecasting, we show how temperature forecasting errors impact the accuracy of different models, with both theoretical derivation and practical experiments. We propose an algorithm for the computation of a "similar day", to compensate for the lack of recent data using seasonality. Finally, we show how different models are better at capturing different properties of the series, and how ensembling approaches may improve the overall accuracy.

Besides the proposed examples, the talk is intended to point out that the literature survey is a critical yet preliminary phase of a Machine Learning project aiming at creating business value. Existing work shall often be modified and extended to comply with extended constraints and more complex datasets.

Delivered at Applied Machine Learning Days, 25-29 January 2020, Lausanne, Switzerland
Recorded session at https://www.youtube.com/watch?v=FZwOrGD3agA
Slides at https://www.slideshare.net/EmanueleFabbiani/academic-and-business-research-in-ai-for-energy