  This paper presents the real-world smart-meter dataset and offers an analysis
of solutions derived from the Energy Prediction Technical Challenges, focusing
primarily on two key competitions: the IEEE Computational Intelligence Society
(IEEE-CIS) Technical Challenge on Energy Prediction from Smart Meter data in
2020 (named EP) and its follow-up challenge at the IEEE International
Conference on Fuzzy Systems (FUZZ-IEEE) in 2021 (named as XEP). These
competitions focus on accurate energy consumption forecasting and the
importance of interpretability in understanding the underlying factors. The
challenge aims to predict monthly and yearly estimated consumption for
households, addressing the accurate billing problem with limited historical
smart meter data. The dataset comprises 3,248 smart meters, with varying data
availability ranging from a minimum of one month to a year. This paper delves
into the challenges, solutions and analysing issues related to the provided
real-world smart meter data, developing accurate predictions at the household
level, and introducing evaluation criteria for assessing interpretability.
Additionally, this paper discusses aspects beyond the competitions:
opportunities for energy disaggregation and pattern detection applications at
the household level, significance of communicating energy-driven factors for
optimised billing, and emphasising the importance of responsible AI and data
privacy considerations. These aspects provide insights into the broader
implications and potential advancements in energy consumption prediction.
Overall, these competitions provide a dataset for residential energy research
and serve as a catalyst for exploring accurate forecasting, enhancing
interpretability, and driving progress towards the discussion of various
aspects such as energy disaggregation, demand response programs or behavioural
interventions.
