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.

翻译：本文介绍了真实的智能电表数据集，并对能源预测技术挑战赛中的解决方案进行了分析，重点关注两大竞赛：2020年IEEE计算智能学会（IEEE-CIS）智能电表数据能源预测技术挑战赛（简称EP）及其在2021年IEEE模糊系统国际会议（FUZZ-IEEE）上的后续挑战赛（简称XEP）。这些竞赛聚焦于能源消耗的精准预测，以及理解潜在因素时可解释性的重要性。挑战赛旨在预测家庭用户的月度及年度估算能耗，解决在历史智能电表数据有限的情况下实现精准计费的问题。该数据集包含3,248个智能电表，数据可用时长从至少一个月到一年不等。本文深入探讨了与提供的真实智能电表数据相关的挑战、解决方案及分析问题，包括在家庭层面实现精准预测，并引入评估可解释性的标准。此外，本文还讨论了竞赛之外的方面：家庭层面能源分解与模式检测应用的机会、沟通能源驱动因素以优化计费的重要性，并强调了负责任的人工智能与数据隐私考量。这些方面为能源消耗预测的更广泛影响和潜在进展提供了见解。总体而言，这些竞赛为居民能源研究提供了数据集，并作为催化剂，推动精准预测的探索、增强可解释性，并促进对能源分解、需求响应计划或行为干预等多方面议题的讨论。