Paper Infomation
Intelligent Home Indoor Environment Comfortable Forecast Based on Neural Network Optimized by Genetic Algorithm
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Author: Xiaohui Wang, Huijuan Bian
Abstract: Aiming at the influence of multiple indoor factors on the comfort of the smart home interior environment, the method of forecasting indoor comfort was researched. Thus on this basis, a back propagation neural network comfortable prediction model optimized by the genetic algorithm was proposed. To find the influencing factors that affecting the indoor comfort, the historical data of the intelligent home data acquisition system was analyzed. The BP neural network model improved by genetic algorithm was established to forecast indoor comfort in advance. Based on the Matlab toolboxes, the trained model was trained by test data, and the output data was compared to the results of traditional method. The results indicate that the application of genetic algorithm optimization is helpful to improve the precision of the BP neural network model and that the percentage of average error between the prediction results and measured results decreases. The proposed forecasting method has significance in engineering applications.
Keywords: Comfort Forecast, Genetic Algorithm, Neural Network
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