Research Article
Research on Interruptible Scheduling Algorithm of Central Air Conditioning Load Under Big Data Analysis
@INPROCEEDINGS{10.1007/978-3-030-36402-1_37, author={Cheng-liang Wang and Yong-biao Yang}, title={Research on Interruptible Scheduling Algorithm of Central Air Conditioning Load Under Big Data Analysis}, proceedings={Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21--22, 2019, Proceedings, Part I}, proceedings_a={ADHIP}, year={2019}, month={11}, keywords={Optimized scheduling strategy Time dimension Interrupt load Internal gene}, doi={10.1007/978-3-030-36402-1_37} }- Cheng-liang Wang
Yong-biao Yang
Year: 2019
Research on Interruptible Scheduling Algorithm of Central Air Conditioning Load Under Big Data Analysis
ADHIP
Springer
DOI: 10.1007/978-3-030-36402-1_37
Abstract
The traditional algorithm is a combination of fuzzy dynamic programming and priority-based heuristic rules. The optimization performance of interruptible load scheduling is poor. For this reason, the central air conditioning load interruptible scheduling algorithm is proposed based on big data analysis. The algorithm adopts the characteristics of central air conditioning load management and selects the time scale of central air conditioning load scheduling. By optimizing the flexibility of interruptible scheduling, based on the central air conditioning load interruptible scheduling model, the optimal individual in the last generation population is decoded by binary coding, so as to realize the central air conditioning load interruptible scheduling algorithm. The experiment proves that the central air conditioning load interruptible scheduling algorithm has strong optimization performance.
