热电一体化高效优化调度策略及运行技术数据集

该数据集包含融合了热电厂、风电场的历史运行数据与MATLAB和EnergyPlus平台下的模拟数据，覆盖东北典型热电系统和河北黄骅群建筑两个应用场景，数据总量达320MB。该数据集包括区域电网电源构成结构仿真数据与群建筑热网蓄热性能仿真数据两大核心子集，内容涵盖逐时热电负荷、风电出力、储能容量系数、建筑热通量、蓄热量等关键参数。为提升数据质量与适用性，研究采用三倍标准差法剔除离群数据，结合线性插值法填补缺失值，并运用K-Means聚类算法进行场景降维。该数据集可广泛应用于数字孪生建模、热电调度优化、风电协同消纳、建筑热惰性评估等研究，具备显著的工程价值与再利用潜力，旨在为新型能源系统构建与智慧供热策略制定提供高质量数据支撑。

This dataset integrates historical operational data from thermal power plants and wind farms, as well as simulation data generated on the MATLAB and EnergyPlus platforms. It covers two application scenarios: the typical thermal power system in Northeast China and the Huanghua building cluster in Hebei Province, with a total data volume of 320 MB. This dataset consists of two core subsets: simulation data on the power source composition structure of regional power grids, and simulation data on the heat storage performance of building cluster heating networks. The content covers key parameters such as hourly thermal and electrical loads, wind power output, energy storage capacity factors, building heat flux, and heat storage capacity. To improve data quality and applicability, this study uses the three-sigma rule to eliminate outlier data, combines linear interpolation to fill missing values, and applies the K-Means clustering algorithm for scenario dimensionality reduction. This dataset can be widely applied to researches such as digital twin modeling, thermal power dispatch optimization, wind power coordinated accommodation, and building thermal inertia evaluation, with significant engineering value and reuse potential. It aims to provide high-quality data support for the construction of new-type energy systems and the formulation of smart heating strategies.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于热电一体化系统的优化调度与运行技术，包含热电厂、风电场的历史运行数据及MATLAB和EnergyPlus平台的模拟数据，覆盖东北典型热电系统和河北黄骅群建筑两个应用场景，数据总量达320MB。数据集核心包括区域电网电源构成仿真和群建筑热网蓄热性能仿真两大子集，涵盖逐时热电负荷、风电出力等关键参数，并采用三倍标准差法、线性插值和K-Means聚类进行数据质量控制。它旨在为数字孪生建模、热电调度优化及智慧供热策略提供高质量数据支撑，具有显著的工程应用价值。