A real-world energy management data set from a smart company building for optimization and machine learning

We present a real-world data set obtained from monitoring a smart company building over the course of six years. The data set describes the energy consumption of various sites within the building, energy production via a photovoltaic system and a combined-heat-and-power plant, and the detailed operation of the heating and cooling system. The data set further contains measurements from an on-site weather station for the same time period. The data set covers periods of normal operation before the onset of the Covid-19-pandemic, periods of reduced operation during, and after, the pandemic. We describe the recording, processing, and curation strategy to generate the data set. The data set enables the application of a wide range of methods in the domain of energy management, including optimization, modelling, and machine learning to optimize building operations and reduce costs and carbon emissions., During the recording time span, a multitude of issues occurred which affected the collected data, like measurement outages, maintenance and device replacements.
In order to produce a consistent and research-grade data set, these issues need to be addressed and corrected. We apply a cleaning and post-processing pipeline to the data, which consists of seven steps:

Specification and detection of issues with rule-based detection mechanism
Data harmonization to ensure consistency in naming and sign convection
Application of issue correction
Time alignment of all measurements
Resampling into equidistantly sampled time series (1 min, 15 min, 1 h)
Calculation of missing dependent measurements
Export the time series in gzip-compressed CSV files

Furthermore, based on the corrected and resampled time series, we provide a reduced dataset. It consists of a less complex representation of the building energy consumption, production of both electricity, heating and cooling, as well as weather measure..., , # A Real-World Energy Management data set from a Smart Company Building for Optimization and Machine Learning

[https://doi.org/10.5061/dryad.73n5tb363](https://doi.org/10.5061/dryad.73n5tb363)

## Description of the data and file structure

The presented data set contains measurements from electricity meters, heat and cooling meters and the weather station from a medium size company, the Honda R&D Europe facility located in Offenbach am Main, Germany.

The data set contains measurements from January 1, 2018 0:00 GMT+1 until January 1, 2024 0:00 GMT+1. Note that the facility is located in Offenbach, Germany, hence the local timezone is Europe/Berlin, which corresponds to GMT+2 during the European daylight savings period, and GMT+1 in winter.

As electricity meters, ABB-B24, Janitza UMG 96 RM-E, Janitza UMG 96 PA MID+, as well as Socomec DIRIS I35, I45 and S135 meters are installed in the facility. Heating and cooling is metered using SensorStar 2/2U meters. Weather measurements are coll...

本研究公开了一套基于六年时长的智能企业楼宇监测所获取的真实世界数据集。该数据集涵盖楼宇内各区域的能耗、光伏系统（photovoltaic system）与热电联产机组（combined-heat-and-power plant）的产能量，以及暖通空调系统的详细运行数据。此外，数据集还包含同期楼宇内气象站的监测数据。
数据集覆盖了新冠疫情暴发前的正常运行阶段、疫情期间及疫情后的运营缩减阶段。本文详述了该数据集的采集、处理与整理策略。本数据集可支撑能源管理领域的多种方法应用，包括优化、建模与机器学习技术，用于优化楼宇运行、降低成本与碳排放。

在数据采集周期内，曾出现多种影响采集结果的问题，例如监测中断、设备维护与更换。为生成一致性强且符合科研级标准的数据集，需对这些问题进行修正与处理。我们针对数据采用了一套包含七个步骤的清洗与后处理流程：
1. 基于规则检测机制的问题定义与识别
2. 数据规范化处理，确保命名与符号约定的一致性
3. 问题修正
4. 所有监测数据的时间对齐
5. 重采样为等间隔时间序列（采样间隔分别为1分钟、15分钟、1小时）
6. 缺失关联监测项的补全计算
7. 将时间序列导出为gzip压缩的CSV文件

此外，基于经修正与重采样后的时间序列，我们还提供了一套简化数据集。该简化数据集采用更简洁的形式呈现楼宇能耗、电力、供热与制冷产能量，以及气象监测数据……
# 一款面向优化与机器学习任务的智能企业楼宇真实世界能源管理数据集
DOI：https://doi.org/10.5061/dryad.73n5tb363

## 数据与文件结构说明
本数据集采集自位于德国美因河畔奥芬巴赫的本田欧洲研发中心（中型企业楼宇）的电表、冷热计量表与气象站的监测数据。
数据集的时间跨度为2018年1月1日00:00（GMT+1）至2024年1月1日00:00（GMT+1）。需注意，该楼宇位于德国奥芬巴赫，当地时区为欧洲/柏林时区：欧洲夏令时期间采用GMT+2，冬季采用GMT+1。

该楼宇安装的电表包括ABB-B24、Janitza UMG 96 RM-E、Janitza UMG 96 PA MID+以及Socomec DIRIS I35、I45与S135型号；冷热计量采用SensorStar 2/2U计量表。气象监测数据为……