Prediction of Indoor Temperature and Relative Humidity Based on Cloud Database by Using an Improved BP Neural Network in Chongqing

For continuous improvement of productivity, accurate, stable, and reliable control of temperature and humidity is important in industrial production. Accurate prediction of air temperature and humidity can improve the predictability and stability of air conditioning control systems. In this paper, based on the cloud database of industry settings, an improved prediction model based on backpropagation (BP) neural networks was established to forecast indoor air temperature (IT) and relative humidity (IH) every 10 min and 6–72 h in advance. The experimental building was in Chongqing, a typical humid, hot-summer, and cold-winter area in China. The test data were used to determine the optimal parameters of the neural network model. The experimental results showed that the IT and IH predictions by our model have strong correlations with the actual data, with the coefficients of determination being 0.9897 and 0.9778, respectively. Compared with other literature, our model was more effective in temperature prediction. The presented method can be used for the prediction and control of the indoor temperature and relative humidity in industrial production.

[1]  Leopold,et al.  Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region , 2016 .

[2]  Eleni Mangina,et al.  Input variable selection for thermal load predictive models of commercial buildings , 2017 .

[3]  Victor C. M. Leung,et al.  Multidimensional context-aware social network architecture for mobile crowdsensing , 2014, IEEE Communications Magazine.

[4]  Tin-Tai Chow,et al.  The use of occupancy space electrical power demand in building cooling load prediction , 2012 .

[5]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[6]  Hao Chen,et al.  Research and Application of Improved Gas Concentration Prediction Model Based on Grey Theory and BP Neural Network in Digital Mine , 2016 .

[7]  Masoud Rahimi,et al.  CFD modeling of flow and heat transfer in a thermosyphon , 2010 .

[8]  Louis Coroller,et al.  Temperature, water activity and pH during conidia production affect the physiological state and germination time of Penicillium species. , 2017, International journal of food microbiology.

[9]  Dequan Zhang,et al.  Fluctuated Low Temperature Combined with High-Humidity Thawing to Reduce Physicochemical Quality Deterioration of Beef , 2014, Food and Bioprocess Technology.

[10]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[11]  Eric Wai Ming Lee,et al.  An intelligent approach to assessing the effect of building occupancy on building cooling load predi , 2011 .

[12]  Jun Cheng,et al.  Mobile Cyber Physical Systems: Current Challenges and Future Networking Applications , 2018, IEEE Access.

[13]  Shrikant Pandey,et al.  Artificial neural networks for predicting indoor temperature using roof passive cooling techniques in buildings in different climatic conditions , 2012, Appl. Soft Comput..

[14]  Manuela Pintado,et al.  Optimization of Temperature, Relative Humidity and Storage Time before and after Packaging of Baby Spinach Leaves Using Response Surface Methodology , 2016, Food and Bioprocess Technology.

[15]  Mohammad Yusri Hassan,et al.  A review on applications of ANN and SVM for building electrical energy consumption forecasting , 2014 .

[16]  C. Anderson‐Cook.  The Cambridge Dictionary of Statistics (2nd ed.) , 2003 .

[17]  Yaohua Hu,et al.  Analysis of Temperature and Humidity Field in a New Bulk Tobacco Curing Barn Based on CFD , 2017, Sensors.

[18]  Bo Chen,et al.  Prediction of PM2.5 Concentration in a Agricultural Park Based on BP Artificial Neural Network , 2016 .

[19]  Han-Cheng Dan,et al.  Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network , 2017 .

[20]  H. B. Nahora,et al.  CFD model of the airflow , heat and mass transfer in cool stores , 2005 .

[21]  H. Metin Ertunc,et al.  Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks , 2006 .

[22]  MengChu Zhou,et al.  Emotion-Aware Cognitive System in Multi-Channel Cognitive Radio Ad Hoc Networks , 2018, IEEE Communications Magazine.

[23]  Türkan Göksal Özbalta,et al.  Models for Prediction of Daily Mean Indoor Temperature and Relative Humidity: Education Building in Izmir, Turkey , 2012 .

[24]  Jaime Gómez Gil,et al.  Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks , 2012, Sensors.

[25]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[26]  Tao Lu,et al.  Prediction of indoor temperature and relative humidity using neural network models: model comparison , 2009, Neural Computing and Applications.

[27]  A. Lipski,et al.  Increased Biomass Production by Mesophilic Food-Associated Bacteria through Lowering the Growth Temperature from 30°C to 10°C , 2016, Applied and Environmental Microbiology.

[28]  Ting Ren,et al.  Temperature prediction of the molten salt collector tube using BP neural network , 2016 .

[29]  Li Zhou,et al.  Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks , 2018, IEEE Internet of Things Journal.

[30]  Gregor P. Henze,et al.  Statistical Analysis of Neural Networks as Applied to Building Energy Prediction , 2004 .

[31]  Lian Zhao,et al.  Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems , 2009 .