包含多重随机扰动因素的单体建筑与热力站动态负荷及全网协同优化调控数据集

本数据集旨在为供热系统负荷预测与优化调控算法研究提供高质量基础数据，重点支撑单体建筑及热力站动态负荷特性分析与全网协同调控策略开发。数据集涵盖多重随机扰动因素影响下的动态负荷数据，包含气象参数、供能参数、室内参数等多维度监测数据，同步整合负荷预测模型训练与验证数据、优化调控参数配置数据及相关算法性能记录。数据处理过程中，基于平台校验后的数据进行系统化预处理，通过统计分析识别并处理异常值，确保数据的可靠性与时空一致性。本数据集可直接用于负荷预测模型的训练与精度验证，为优化调控算法开发提供实测参数与优化结果对照，同时助力揭示多重扰动下负荷动态特性及系统调控规律，为供热系统的智能化运行与能效提升提供关键数据支撑。

This dataset is designed to provide high-quality foundational data for research on heating system load forecasting and optimal regulation and control algorithms, with a focus on supporting the analysis of dynamic load characteristics of individual buildings and thermal substations, as well as the development of coordinated regulation and control strategies for the entire heating network. The dataset contains dynamic load data affected by multiple random disturbance factors, including multi-dimensional monitoring data such as meteorological parameters, energy supply parameters, and indoor parameters. It also integrates training and validation datasets for load forecasting models, parameter configuration data for optimal regulation and control, and performance records of relevant algorithms. During the data processing workflow, systematic preprocessing is conducted on the platform-validated data, and outliers are identified and processed via statistical analysis to ensure the data's reliability and spatiotemporal consistency. This dataset can be directly utilized for the training and accuracy validation of load forecasting models, provides a comparison between measured parameters and optimization outcomes for the development of optimal regulation and control algorithms, and helps reveal the dynamic load characteristics and system regulation rules under multiple disturbances, thus offering critical data support for the intelligent operation and energy efficiency enhancement of heating systems.

搜集汇总

背景与挑战

背景概述

该数据集旨在为供热系统的负荷预测与优化调控研究提供基础数据，重点关注多重随机扰动因素影响下的动态负荷特性。它包含气象、供能、室内等多维度监测数据，以及负荷预测模型训练与优化调控参数配置信息。经过预处理确保数据可靠性，可直接用于模型训练、算法开发和揭示系统调控规律，支持供热系统智能化运行与能效提升。