Winning the Big Data Technologies Horizon Prize: Fast and reliable forecasting of electricity grid traffic by identification of recurrent fluctuations

TL;DR

This paper describes a methodology for accurate, reliable, and efficient forecasting of electricity grid traffic by identifying recurrent fluctuations and refining predictions through regression, winning the EU Big Data Horizon Prize.

Contribution

It introduces a novel approach combining fluctuation identification and regression refinement for electricity traffic forecasting, emphasizing robustness and adaptability.

Findings

Effective identification of recurrent fluctuations improves forecast accuracy.

Regression-based refinement enhances reliability under noisy and incomplete data.

Methodology demonstrates robustness and efficiency in practical applications.

Abstract

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.