Winning the Big Data Technologies Horizon Prize: Fast and reliable forecasting of electricity grid traffic by identification of recurrent fluctuations
This addresses the problem of reliable grid management for energy providers, though it appears incremental as it builds on existing fluctuation-based methods.
The paper tackled forecasting electricity grid traffic by identifying short-term recurrent fluctuations and refining them through regression, winning the EU Big Data Technologies Horizon Prize.
This paper provides a description of the approach and methodology I used in winning the European Union Big Data Technologies Horizon Prize on data-driven prediction of electricity grid traffic. The methodology relies on identifying typical short-term recurrent fluctuations, which is subsequently refined through a regression-of-fluctuations approach. The key points and strategic considerations that led to selecting or discarding different methodological aspects are also discussed. The criteria include adaptability to changing conditions, reliability with outliers and missing data, robustness to noise, and efficiency in implementation.