Abstract
Artificial neural network has been widely used in air conditioning systems as an effective method for predicting parameters, and the accuracy of ANN model relies on training data and network structure. In order to increase the quality of chilled water loops model, this paper develops an optimal data processing algorithm combining Kalman filtering with particle swarm optimization to compensate for uncertain factors and disturbances of collected data from the case building and establishes the nonlinear variation trend database. Based on Elman and BP neural networks, this paper proposes the improved network structures to avoid the local optimum predicted value of chilled water loops and increase data training speed. Simulation results show that this algorithm improves the data accuracy of current percentage (CP) of chillers and chilled water temperatures 12% and 9%. Compared with Elman and BP models, mean absolute errors of CP improved models are improved 24.1% and 10.3%, and mean squared errors of water temperature improved models are improved 5.2% and 4.8%. For the purpose of energy conservation control in air conditioning systems, this work has an application value and can be used for predicting other parameters of buildings.
Highlights
With the development of artificial pattern recognition and intelligence technology, the information construction of air conditioning systems has been improved
In order to increase the quality of chilled water loops model, this paper develops an optimal data processing algorithm combining Kalman filtering with particle swarm optimization to compensate for uncertain factors and disturbances of collected data from the case building and establishes the nonlinear variation trend database
For taking advantage of measured data and building more precise prediction model, this paper develops an optimization data processing scheme combining Kalman filtering with particle swarm optimization (KPOS) to improve the accuracy of measured data and analyzes the data variation trends for establishing the nonlinear variation trend database (NVTD)
Summary
With the development of artificial pattern recognition and intelligence technology, the information construction of air conditioning systems has been improved. When measured data are transmitted to servers using communication technology, it will produce great value How to use these huge data to resolve the management and optimal configuration problems for the limited energy resources is always the hot topic, which depends on the support of the relevant data processing and an analysis tool. Chang et al [1] developed an effective data acquisition and air conditioning control system using Labview; it could realize the real-time data acquisition as well as the data transmission, processing, and display. This system could save a lot of manual operations and material resources.
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