Improved particle filter based soft sensing of room cooling load
| Autor: | Li Zhanpei, Liu Tingzhang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Engineering
business.industry 020209 energy Mechanical Engineering Cooling load Process (computing) Mechanical engineering 02 engineering and technology Building and Construction 010501 environmental sciences 01 natural sciences Bottleneck Demand response Energy conservation Air conditioning 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering business Particle filter Frequency domain decomposition Simulation 0105 earth and related environmental sciences Civil and Structural Engineering |
| Zdroj: | Energy and Buildings. 142:56-61 |
| ISSN: | 0378-7788 |
| DOI: | 10.1016/j.enbuild.2017.03.010 |
| Popis: | Accurate value of room cooling load is the basis data for energy conservation and demand response in the air conditioning operating process. Unfortunately, traditional calculation methods for room cooling load are too complex and time-consuming to meet the demand of real-time control. Soft sensing is an attractive technology, however the room cooling load cannot be measured directly, so the parameter identification for soft sensing model cannot be realized based on the sample data. Aiming at the bottleneck problem in room cooling load soft sensing, an improved particle filter based method is presented in this article. Firstly, a model for room cooling load soft sensing is built with analysis of room energy balance equation. Then frequency domain decomposition is employed for rough measurement, and deep learning is employed for prediction. Finally, the improved particle filter is employed to realize the real-time state estimation of room cooling load. In the process of particle filter, the artificial fish swarm algorithm is introduced to overcome the sample impoverishment problem of traditional Re-sample method. The simulation experiments and real-test experiments show that the proposed method can realize the soft sensing of room cooling load quickly and efficiently, which method also provide references for the soft sensing of other un-measurable variables. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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