2026-10-13T02:40:19Z https://www.scidb.cn/oai

10.57760/sciencedb.j00173.00047 2026-05-14T09:39:18Z

2026-05-14 260149 EP丨A Comprehensive Simulation and Algorithm Evaluation Dataset for Large-Scale Heterogeneous HVAC Cluster Scheduling in Demand Response doi:10.57760/sciencedb.j00173.00047 en Paper URL：https://jeit.ac.cn/cn/article/doi/10.11999/JEIT260149In order to meet the development needs of load side resource demand response in the new power system, and to address the lack of standardized and complex testing scenarios in existing large-scale HVAC cluster scheduling research, our team has constructed a high fidelity HVAC cluster simulation system based on an equivalent thermal parameter model, which includes complex factors such as multi-source thermal disturbances and upstream and downstream communication packet loss, and generated the experimental dataset for this simulation. This dataset aims to simulate high-dimensional and nonlinear cluster control environments in real distribution networks. The data mainly includes the thermal and physical parameters of large-scale heterogeneous air conditioning nodes, multi-level user comfort preference setting intervals, and corresponding rigid peak shaving and valley filling scheduling instruction sequences and environmental temperature random disturbance sequences. The entire dataset covers the complete attribute features of 1000 heterogeneous HVAC nodes. This dataset and its supporting scenarios can be directly used for performance testing, high-dimensional optimization capability verification, and robustness evaluation of cluster collaborative scheduling algorithms such as model predictive control and swarm intelligence optimization. For the convenience of researchers reproducing the experimental results of the paper and conducting comparative verification, we now publicly disclose the core parameter configuration file and some environmental test subsets of the simulation scenario for academic exchange. Demand Response; HVAC Clusters; Peak Shaving and Valley Filling; ETP Model; Particle Swarm Optimization Zheng Bowen Pan Mingming wang lei Liu Chang Zheng Qingrong Tang Zhuofan Zhao Jianli PUBLIC https://api.github.com/licenses/cc0-1.0 dataset Science Data Bank