Open AccessExploring New Building Energy Saving Control Strategy Application under the Energy Internet of Things
Author(s) -
Yude Qin,
Ke Ji,
B Wang
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/701/1/012060
Subject(s) - flexibility (engineering) , scalability , computer science , model predictive control , key (lock) , process (computing) , the internet , control (management) , energy consumption , building automation , big data , control engineering , machine learning , artificial intelligence , data mining , engineering , database , statistics , physics , mathematics , computer security , world wide web , electrical engineering , thermodynamics , operating system
This paper makes innovative research on developing a data-driven control strategy under the Energy Internet of Things architecture. On the one hand, the platform aims to provide data representation and interpretable analysis for different stakeholders (end users, construction operators or managers) to realize the flexibility and scalability of the platform; on the other hand, it can improve the thermal comfort and also reduce the power consumption of buildings. However, to process vast amounts of data, it is critical to select appropriate control methods and design optimization issues. Data-driven predictive control (DPC) is a control technology that replaces model-based predictive control (MPC). When applied to complex building operations, MPC is implemented by using the control-oriented data-driven model. The key to DPC technology is the use of CatBoost algorithm, which is highly interpretable and easy to be operated by stakeholders. This paper chooses TDNN, LightGBM, and CatBoost to compare and analyze building energy consumption. Numerical simulation results show that the CatBoost algorithm’s performance is better than other algorithms, and the complexity and implementation cost is significantly reduced.