Design and Implementation of a Cloud Enabled Random Neural Network-Based Decentralized Smart Controller With Intelligent Sensor Nodes for HVAC

Building energy management systems (BEMSs) monitor and control the heating ventilation and air conditioning (HVAC) of buildings in addition to many other building systems and utilities. Wireless sensor networks (WSNs) have become the integral part of BEMS at the initial implementation phase or latter when retro fitting is required to upgrade older buildings. WSN enabled BEMS, however, have several challenges which are managing data, controllers, actuators, intelligence, and power usage of wireless components (which might be battery powered). The wireless sensor nodes have limited processing power and memory for embedding intelligence in the sensor nodes. In this paper, we present a random neural network (RNN)-based smart controller on a Internet of Things (IoT) platform integrated with cloud processing for training the RNN which has been implemented and tested in an environment chamber. The IoT platform is modular and not limited to but has several sensors for measuring temperature, humidity, inlet air coming from the HVAC duct and PIR. The smart RNN controller has three main components: 1) base station; 2) sensor nodes; and 3) the cloud with embedded intelligence on each component for different tasks. This IoT platform is integrated with cloud processing for training the RNN. The RNN-based occupancy estimator is embedded in sensor node which estimates the number of occupants inside the room and sends this information to the base station. The base station is embedded with RNN models to control the HVAC on the basis of setpoints for heating and cooling. The HVAC of the environment chamber consumes 27.12% less energy with smart controller as compared to simple rule-based controllers. The occupancy estimation time is reduced by our proposed hybrid algorithm for occupancy estimation that combines RNN-based occupancy estimator with door sensor node (equipped with PIR and magnetic reed switch). The results show that accuracy of hybrid RNN occupancy estimator is 88%.

[1]  Erol Gelenbe,et al.  Analog hardware implementation of the random neural network model , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[2]  Volkan Atalay,et al.  Texture Classification and Retrieval Using the Random Neural Network Model , 2006, 6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004..

[3]  Philip Haves,et al.  Model predictive control for the operation of building cooling systems , 2010, Proceedings of the 2010 American Control Conference.

[4]  Ruxu Du,et al.  Thermal comfort control based on neural network for HVAC application , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[5]  Young-Jin Kim,et al.  Cloud-based demand response for smart grid: Architecture and distributed algorithms , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[6]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[7]  M Morari,et al.  Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions , 2010, Proceedings of the 2010 American Control Conference.

[8]  Sean Danaher,et al.  Application of an Artificial Neural Network for Modelling the Thermal Dynamics of a Building’s Space and its Heating System , 2002 .

[9]  Thomas Weng,et al.  Occupancy-driven energy management for smart building automation , 2010, BuildSys '10.

[10]  Stanley A. Mumma,et al.  Transient Occupancy Ventilation By Monitoring CO 2 , 2004 .

[11]  Jong-Jin Kim,et al.  ANN-based thermal control models for residential buildings , 2010 .

[12]  Erol Gelenbe,et al.  Random Neural Networks with Negative and Positive Signals and Product Form Solution , 1989, Neural Computation.

[13]  Zhi-Hong Mao,et al.  Function Approximation by Random Neural Networks with a Bounded Number of Layers , 2006 .

[14]  Yi Shen,et al.  Image Segmentation Based on Random Neural Network Model and Gabor Filters , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[15]  John Psarras,et al.  Intelligent building energy management system using rule sets , 2007 .

[16]  Abbas Javed,et al.  Comparison of the Robustness of RNN, MPC and ANN Controller for Residential Heating System , 2014, 2014 IEEE Fourth International Conference on Big Data and Cloud Computing.

[17]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[18]  Kamin Whitehouse,et al.  The self-programming thermostat: optimizing setback schedules based on home occupancy patterns , 2009, BuildSys '09.

[19]  Miao Yun,et al.  Research on the architecture and key technology of Internet of Things (IoT) applied on smart grid , 2010, 2010 International Conference on Advances in Energy Engineering.

[20]  Abbas Javed,et al.  Experimental testing of a random neural network smart controller using a single zone test chamber , 2015, IET Networks.

[21]  Alessandro Bassi,et al.  From today's INTRAnet of things to a future INTERnet of things: a wireless- and mobility-related view , 2010, IEEE Wireless Communications.

[22]  Erol Gelenbe Stability of the Random Neural Network Model , 1990, EURASIP Workshop.

[23]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[24]  Yacine Rezgui,et al.  Cloud Supported Building Data Analytics , 2014, 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[25]  Karl Henrik Johansson,et al.  Estimation of building occupancy levels through environmental signals deconvolution , 2013, BuildSys@SenSys.

[26]  Lukas Ferkl,et al.  Model predictive control of a building heating system: The first experience , 2011 .

[27]  António E. Ruano,et al.  Prediction of building's temperature using neural networks models , 2006 .

[28]  Gerardo Rubino,et al.  A study of real-time packet video quality using random neural networks , 2002, IEEE Trans. Circuits Syst. Video Technol..

[29]  A. Ebenezer Jeyakumar,et al.  Hybrid PSO–SQP for economic dispatch with valve-point effect , 2004 .

[30]  Aguilar Jose Definition of an energy function for the random neural to solve optimization problems. , 1998, Neural networks : the official journal of the International Neural Network Society.

[31]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[32]  Erol Gelenbe,et al.  Task Assignment and Transaction Clustering Heuristics for Distributed Systems , 1997, Inf. Sci..

[33]  Kamin Whitehouse,et al.  The smart thermostat: using occupancy sensors to save energy in homes , 2010, SenSys '10.

[34]  W. Kurschl,et al.  Combining cloud computing and wireless sensor networks , 2009, iiWAS.

[35]  Xuan Li,et al.  Pricing and peak aware scheduling algorithm for cloud computing , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[36]  Myoung-Souk Yeo,et al.  Application of artificial neural network to predict the optimal start time for heating system in building , 2003 .

[37]  Ching-Hsien Hsu,et al.  Implementation of Smart Power Management and Service System on Cloud Computing , 2012, 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing.

[38]  Nabil Nassif,et al.  A robust CO2-based demand-controlled ventilation control strategy for multi-zone HVAC systems , 2012 .

[39]  Taskin Koçak,et al.  Learning in the feed-forward random neural network: A critical review , 2011, Perform. Evaluation.

[40]  Myoung-Souk Yeo,et al.  Predictive Control of the Radiant Floor Heating System in Apartment Buildings , 2002 .

[41]  Isik Aybay,et al.  A digital neuron realization for the random neural network model , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[42]  Klaus Schittkowski,et al.  A comparative performance evaluation of 27 nonlinear programming codes , 1983, Computing.

[43]  Zhi-Hong Mao,et al.  Function approximation with spiked random networks , 1999, IEEE Trans. Neural Networks.

[44]  Madoka Yuriyama,et al.  Sensor-Cloud Infrastructure - Physical Sensor Management with Virtualized Sensors on Cloud Computing , 2010, 2010 13th International Conference on Network-Based Information Systems.

[45]  Sanjit Kumar Dash,et al.  Sensor-Cloud: Assimilation of Wireless Sensor Network and the Cloud , 2012 .

[46]  Erol Gelenbe,et al.  Random Neural Networks with Synchronized Interactions , 2008, Neural Computation.

[47]  Stelios Timotheou,et al.  The Random Neural Network: A Survey , 2010, Comput. J..

[48]  Erol Gelenbe,et al.  Random neural network texture model , 2000, Electronic Imaging.

[49]  Yan Li,et al.  Cloud Service based intelligent power monitoring and early-warning system , 2012, IEEE PES Innovative Smart Grid Technologies.

[50]  Sehyun Park,et al.  Cloud Computing-Based Building Energy Management System with ZigBee Sensor Network , 2012, 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[51]  Desmond Gibson,et al.  A Novel Solid State Non-Dispersive Infrared CO2 Gas Sensor Compatible with Wireless and Portable Deployment , 2013, Sensors.

[52]  Hani Hagras,et al.  Programming iSpaces — A Tale of Two Paradigms , 2006 .

[53]  Alberto Cerpa,et al.  Occupancy based demand response HVAC control strategy , 2010, BuildSys '10.

[54]  Gregory M. P. O'Hare,et al.  A Review of Wireless-Sensor-Network-Enabled Building Energy Management Systems , 2014, ACM Trans. Sens. Networks.