AlphaBuilding - Synthetic Buildings Operation Dataset

This is a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years' historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants' diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

本数据集为合成建筑运行数据集，涵盖暖通空调（HVAC）、照明、杂项电气负载（MELs）系统运行工况、人员数量、环境参数、终端用能及建筑总能耗数据，采样间隔为10分钟。该数据集基于美国能源部（U.S. Department of Energy，简称DOE）详细中型基准办公建筑模型，结合迈阿密、旧金山、芝加哥三个典型气候区的30年历史气象数据，通过1395次年度模拟生成。该数据集考虑了建筑及系统的三种能效等级。为还原真实建筑运行场景，数据集采用了关于人员流动、人员多样化温度偏好、照明及杂项电气负载的相关假设。数据集采用语义化建筑元数据架构BRICK存储建筑元数据。该数据集以1.2TB的压缩HDF5文件格式存储。本数据集可应用于诸多场景，包括建筑能源与负荷曲线基准测试、能源模型校准、人员与气象变异性及其对建筑性能的影响评估、热负荷与能源负荷预测相关算法的开发与测试、模型预测控制，以及基于强化学习的建筑控制策略制定。

搜集汇总

背景与挑战

背景概述

AlphaBuilding 是一个合成建筑运行数据集，基于美国能源部中型参考办公楼模型和三个气候区30年历史天气数据，通过1395次模拟生成，包含HVAC、照明、能耗等多参数10分钟间隔数据。该数据集考虑了不同能效水平和现实运行假设，使用BRICK元数据模式存储，以1.2 TB压缩HDF5文件形式提供，适用于能源基准测试、模型校准和算法开发等应用。