FUZZ-IEEE Competition on Explainable Energy Prediction

Predicting energy consumption is currently a key challenge for the energy industry as a whole.  Predicting the consumption in a certain area is massively complicated due to the sudden changes in the way that energy is being consumed and generated at the current point in time. However, this prediction becomes extremely necessary to minimise costs and to enable adjusting (automatically) the production of energy and better balance the load between different energy sources.

Smart meters are being rolled-out in many countries for domestic use, becoming powerful devices to track energy use. Smart meters will not only play an increasingly large role in the way customers consume energy but also in the way they choose a supplier. If energy providers developed new products for their customers that help them understand the potential benefits of smart meters (e.g. reducing bills or managing their finances better), this could be key to better serving, and thus attracting and retaining customers.  At a higher level, being able to use smart meter data to better manage demand will contribute to the increasing use of sustainable sources of power and reduce the demands on more traditional power generation.

In early 2020, the IEEE Computational Intelligence Society (IEEE-CIS) partnered up with one of the leading international energy provider, E.ON SE, seeking the best solutions for energy prediction using smart meters, and held a competition with great success (see more here). The goal of that competition was to predict monthly and yearly consumption from a limited amount of data, in which the evaluation has been focused on relative errors at predicting energy consumption.

In this competition, the same prediction problem applies. However, the challenge is on building not only accurate but also explainable predictions. From a machine learning point of view, the provided data is very challenging and may lead to the development of novel learning approaches. Some of the challenges include incomplete data (i.e. missing values), use of external data sources to handle seasonal effects, different kinds of households (e.g. families vs. single, old house vs. new built), or lack of sufficient information about the households. From the point of view of Explainable Artificial Intelligence, we are looking for self-explaining models, i.e., accurate predictions coming along with a narrative explanation in natural language easy to understand by customers.

This competition will be hosted at the 2021 FUZZ-IEEE Conference (Luxembourg):

https://attend.ieee.org/fuzzieee-2021/competition/

Please report any issues or feedback to Isaac.Triguero@nottingham.ac.uk