Abstract
A combination of JMP, PSO-BP neural network, and Markov chain which aims at the low correlation between input and output data and the error of prediction model in the PSO-BP neural network prediction model is proposed. First, the JMP data processing software is used to process the input data and eliminate the samples with low coupling degree. Then, obtaining the cooling load prediction results relies on the training from the PSO-BP neural network. Finally, the final prediction results will be generated by eliminating the random errors using the Markov chain. The results show that the combination of the prediction methods has higher prediction accuracy and conforms to the change rule of the cooling load in shopping malls. Besides, the combination fits the actual application requirements as well.
Highlights
At present, the energy consumption of air-conditioning occupies an increasing proportion of whole building energy consumption [1]
Statistical regression is a simple and feasible prediction method, but its predictive power is lower than support vector machine (SVM), and it is difficult to choose the predictors of this method [7]. e decision tree method is a technique which is easy to understand and divides data into groups using a tree diagram, but its prediction results often deviate significantly from the actual situations [8], and it cannot address time series and nonlinear data very well [9]
Based on the traditional artificial neural network, this paper shows the results from JMP data analysis software, which analyzes the correlation between input variables and output variables and eliminates irrelevant data
Summary
The energy consumption of air-conditioning occupies an increasing proportion of whole building energy consumption [1]. For the air-conditioning cooling load prediction, most researchers use the data-driven method [3], mainly including support vector machine (SVM), statistical regression, decision tree, genetic algorithm, and neural network algorithm. E PSO-BP neural network is used to predict the cooling load of air-conditioning in shopping malls.
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