[1]LI Qinghua,RAN Yongyi,LIU Qichen,et al.Proactive intelligent energy-saving optimization algorithm for data center CCHP system[J].CAAI Transactions on Intelligent Systems,2025,20(1):139-149.[doi:10.11992/tis.202312037]
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Proactive intelligent energy-saving optimization algorithm for data center CCHP system

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 1 Page number: 139-149 Column: 学术论文—智能系统 Public date: 2025-01-05
Title:
Proactive intelligent energy-saving optimization algorithm for data center CCHP system
Author(s):
LI Qinghua1;  RAN Yongyi1;  LIU Qichen2;  SUN Tongyao1;  CHEN Shuangwu3;  LUO Jiangtao1
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. School of Architecture, Planning and Landscape, Newcastle University, Newcastle NE1 7RU, England;
3. School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China
Keywords:
green data center; combined cooling;  heating and power generation; intelligent energy-saving; deep reinforcement learning; carbon emission optimization; energy efficiency improvement; joint control; predictive network
CLC:
TP272
DOI:
10.11992/tis.202312037
Abstract:
The existing methods for energy-saving and carbon reduction optimization in data centers lack a comprehensive consideration of the coupling of carbon footprint-related factors, including energy input, production consumption, and waste utilization. This limitation hinders the achievement of systematic energy-saving and carbon reduction. To address this issue, a deep reinforcement learning-based optimization algorithm, named DeepCCHP, is proposed. This algorithm focuses on the combined cooling, heating and power generation (CCHP) in data centers, employing coordinated control of the power supply and cooling systems to optimize electricity cost, carbon emissions, and PUE. DeepCCHP integrates a long and short-term time-series network-attention (LSTNet-Attn) for multi-dimensional time series forecasting and a deep reinforcement learning approach to solve the joint optimization problem, achieving proactive joint control of power generation and cooling equipment. The algorithm is validated through training and verification using Alibaba data center cluster data in a simulation environment based on Trnsys software. Experimental results demonstrate that, compared with baseline algorithms, the DeepCCHP algorithm can achieve up to 40% cost savings and 28% reduction in carbon emissions. It also demonstrates a better trade-off and balance among energy cost, carbon emissions, and energy efficiency.
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