IEEE-CIS 3rd Technical Challenge (IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling)

IEEE 计算智能协会于 2021 年 7 月至 2021 年 11 月举办了一场基于可再生能源数据进行预测和优化的竞赛。数据来自澳大利亚维多利亚州墨尔本的莫纳什大学克莱顿校区的六座建筑物和六个太阳能装置。该数据的分辨率为 15 分钟，范围为 2016 年至 2020 年。鉴于澳大利亚 BOM 和欧洲 ECMWF 的完美天气预报，竞争对手必须预测 2020 年 10 月和 2020 年 11 月的太阳能发电和建筑用电量。然后，他们必须提交课程表，以根据当月的高峰需求和（已知的）电价将电费降至最低。引文：C. Bergmeir、F. de Nijs 等人。 “IEEE-CIS 对可再生能源调度的预测+优化技术挑战”，2021 年。[在线]。可用：https://dx.doi.org/10.21227/1x9c-0161

The IEEE Computational Intelligence Society (IEEE CIS) hosted a competition focused on prediction and optimization using renewable energy data from July 2021 to November 2021. The dataset was collected from six buildings and six solar installations at the Clayton Campus of Monash University, Melbourne, Victoria, Australia. The data has a 15-minute temporal resolution and covers the period from 2016 to 2020. Given the perfect weather forecasts provided by Australia’s BOM and Europe’s ECMWF, participants were required to predict solar power generation and building electricity consumption for October and November 2020. They then needed to submit an optimized schedule to minimize electricity costs based on that month’s peak demand and the (known) electricity prices. Citation: C. Bergmeir, F. de Nijs et al. "IEEE-CIS Forecasting+Optimization Technical Challenge for Renewable Energy Scheduling", 2021. [Online]. Available: https://dx.doi.org/10.21227/1x9c-0161

搜集汇总

背景与挑战

背景概述

该数据集源自IEEE计算智能协会2021年举办的可再生能源调度预测与优化竞赛，包含澳大利亚莫纳什大学2016至2020年建筑物和太阳能装置的15分钟分辨率数据。竞赛要求基于天气预报预测能源供需并优化调度方案，以最小化电费成本。