Real-time prediction model for indoor temperature in a commercial building

Indoor environmental parameters have significant influence on commercial building energy consumption and indoor thermal comfort. Prediction of these parameters, especially that of indoor air temperature, along with continuous monitoring and control of real world parameters can aid in the management of energy consumption and thermal comfort levels in existing buildings. An accurate indoor temperature prediction model assists in achieving an effective energy management strategy such as resetting air temperature set-points in commercial buildings. This study examines the real indoor environmental data for multiple adjacent zones in a commercial building in the context of thermal comfort and identifies the possibility of resetting air temperature set-point without compromising the occupant comfort level. Also, the value of predicting the indoor temperature accurately in such a building is established through this case study. This study presents a nonlinear autoregressive network with exogenous inputs-based system identification method to predict indoor temperature. During model development efforts have been paid to optimize the performance of the model in terms of complexity, prediction results and ease of application to a real system. The performance of single-zone and multi-zone prediction models is evaluated using different combinations and sizes of training data-sets. This study confirms that evaluating the performance of the model in the context of major contributing aspects such as optimal input parameters and network size, optimum size of training data, etc. offers optimized model performance. Thus, when the developed model is used for long-term prediction, it provides better prediction performance for an extended time span compared to existing studies. Therefore, it is anticipated that implementation of this long-term prediction model will offer greater energy savings and improved indoor environmental conditions through optimizing the set-point temperatures.

[1]  Xiaosi Zeng,et al.  Development of Recurrent Neural Network Considering Temporal‐Spatial Input Dynamics for Freeway Travel Time Modeling , 2013, Comput. Aided Civ. Infrastructure Eng..

[2]  Zhenjun Ma,et al.  Supervisory and Optimal Control of Building HVAC Systems: A Review , 2008 .

[3]  Vítor Leal,et al.  Modelling the relationship between heating energy use and indoor temperatures in residential buildings through Artificial Neural Networks considering occupant behavior , 2017 .

[4]  Noureddine Zerhouni,et al.  Defining and applying prediction performance metrics on a recurrent NARX time series model , 2010, Neurocomputing.

[5]  F. Haghighat,et al.  Indoor thermal condition in urban heat island: Comparison of the artificial neural network and regression methods prediction , 2014 .

[6]  Yunpeng Wang,et al.  Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .

[7]  James E. Braun,et al.  An Inverse Gray-Box Model for Transient Building Load Prediction , 2002 .

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

[9]  Lei Chen,et al.  A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings , 2015 .

[10]  Rubiyah Yusof,et al.  A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system , 2016 .

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

[12]  A. B. M. Shawkat Ali,et al.  Predicting Vertical Acceleration of Railway Wagons Using Regression Algorithms , 2010, IEEE Transactions on Intelligent Transportation Systems.

[13]  S. Chandel,et al.  Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models , 2014 .

[14]  Julio Ariel Romero,et al.  A simplified black-box model oriented to chilled water temperature control in a variable speed vapour compression system , 2011 .

[15]  Andrea Costa,et al.  Building operation and energy performance: Monitoring, analysis and optimisation toolkit , 2013 .

[16]  Andrew Kusiak,et al.  Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms , 2015 .

[17]  Shengwei Wang,et al.  Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .

[18]  Jili Zhang,et al.  Predication control for indoor temperature time-delay using Elman neural network in variable air volume system , 2017 .

[19]  Radiša Jovanović,et al.  Ensemble of various neural networks for prediction of heating energy consumption , 2015 .

[20]  Nabil Nassif,et al.  Self-Tuning Dynamic Models of HVAC System Components , 2008 .

[21]  Matthias Dehmer,et al.  Statistical and Machine Learning Approaches for Network Analysis , 2012 .

[22]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[23]  Tao Lu,et al.  Modeling and forecasting energy consumption for heterogeneous buildings using a physical -statistical approach , 2015 .

[24]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[25]  Fan Zhang,et al.  A review on time series forecasting techniques for building energy consumption , 2017 .

[26]  Aleksandra Sretenovic Analysis of energy use at university campus , 2013 .

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

[28]  Gm. Shafiullah,et al.  Modeling techniques used in building HVAC control systems: A review , 2017 .

[29]  Farrokh Janabi-Sharifi,et al.  Gray-box modeling and validation of residential HVAC system for control system design , 2015 .

[30]  Chirag Deb,et al.  Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks , 2016 .

[31]  Burcin Becerik-Gerber,et al.  Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings , 2016 .

[32]  Parham A. Mirzaei,et al.  Simplified model to predict the thermal demand profile of districts , 2017 .

[33]  Carlos Henggeler Antunes,et al.  An Integrated Building Energy Management System , 2017 .

[34]  K. P. Sudheer,et al.  Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions , 2010, Environ. Model. Softw..

[35]  Jiejin Cai,et al.  Applying support vector machine to predict hourly cooling load in the building , 2009 .

[36]  Eric Wai Ming Lee,et al.  Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach , 2018, Applied Energy.

[37]  Richard de Dear,et al.  A preliminary evaluation of two strategies for raising indoor air temperature setpoints in office buildings , 2011 .

[38]  Gary Higgins,et al.  Prediction of indoor temperature in an institutional building , 2017 .

[39]  Jie Chen,et al.  Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural n , 2011 .

[40]  Nursyarizal Mohd Nor,et al.  A review on optimized control systems for building energy and comfort management of smart sustainable buildings , 2014 .

[41]  Jie Chen,et al.  Thermal behaviour prediction utilizing artificial neural networks for an open office , 2010 .

[42]  Michael R. Brambley,et al.  Advanced Sensors and Controls for Building Applications: Market Assessment and Potential R&D Pathways , 2005 .

[43]  Gabriele Comodi,et al.  The role of data sample size and dimensionality in neural network based forecasting of building heating related variables , 2016 .

[44]  Farrokh Janabi-Sharifi,et al.  Black-box modeling of residential HVAC system and comparison of gray-box and black-box modeling methods , 2015 .

[45]  Gary Higgins,et al.  Technological advancement of energy management facility of institutional buildings: A case study , 2017 .

[46]  Raad Z. Homod,et al.  Review on the HVAC System Modeling Types and the Shortcomings of Their Application , 2013 .

[47]  Peter Tiño,et al.  Learning long-term dependencies in NARX recurrent neural networks , 1996, IEEE Trans. Neural Networks.

[48]  Huanxin Chen,et al.  Machine learning-based thermal response time ahead energy demand prediction for building heating systems , 2018, Applied Energy.

[49]  Pengjie Qin,et al.  Prediction of Indoor Temperature and Relative Humidity Based on Cloud Database by Using an Improved BP Neural Network in Chongqing , 2018, IEEE Access.

[50]  Hui Zhang,et al.  EXTENDING AIR TEMPERATURE SETPOINTS: SIMULATED ENERGY SAVINGS AND DESIGN CONSIDERATIONS FOR NEW AND RETROFIT BUILDINGS , 2015 .

[51]  Jörn von Grabe,et al.  Potential of artificial neural networks to predict thermal sensation votes , 2016 .

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

[53]  Fu Xiao,et al.  A short-term building cooling load prediction method using deep learning algorithms , 2017 .