Development of a method of real-time building energy simulation for efficient predictive control

Abstract This study is to develop a real-time building energy simulation method for efficient predictive control of building. One of the most critical point in predictive control is that accurate prediction should take precedence before reaching to control value. Some researches using model predictive control (MPC) based on simulations may result in error due to uncertain data input. In order to reduce error, current circumstances should be applied to the simulation; improving weather forecast data can increase the accuracy. Thus, this study focuses on developing simulation method using daily updated weather forecast data. The novel building energy simulation method proposed in this study is real-time building energy simulation in which generates a real-time weather data file. The data file consists of calculations from web-based forecast data, equations, built-in functions of EnergyPlus, and other weather elements (default) from building controls virtual test bed (BCVTB). In other words, the building energy simulation, designed in this study is an EnergyPlus-specific real-time weather data file generation method. The method generates a 24-h weather data file every day. When the weather is predicted using the proposed method, mean bias error (MBE) and coefficient of variation of root mean squared error (Cv(RMSE)) are calculated to be 1.3% and 20.1%, respectively. A case study for real-time building energy simulation is carried out, finding optimal set-point temperature of chilled water and condenser water of 8.5 °C and 27.75 °C, respectively. The implementation of the values results 23.5 kW h potential savings during the day time, compared to the predicted energy consumption. Thus, it is expected to draw more reasonable predicted control value, using the real-time building energy simulation.

[1]  Shengwei Wang,et al.  A robust model predictive control strategy for improving the control performance of air-conditioning systems , 2009 .

[2]  J. C. Lam,et al.  Generation of typical weather years with identified standard skies for Hong Kong , 2012 .

[3]  Michael Wetter,et al.  GenOpt(R), generic optimization program, User Manual, Version 2.0.0 , 2003 .

[4]  S. H Cho,et al.  Predictive control of intermittently operated radiant floor heating systems , 2003 .

[5]  Geoff Levermore,et al.  Building Energy Management Systems; application to low-energy HVAC and natural ventilation control , 2000 .

[6]  Philip Haves,et al.  A Modular Building Controls Virtual Test Bed for the Integrations of Heterogeneous Systems , 2008 .

[7]  Tetsuo Hayashi,et al.  PROCEDURES FOR SEPARATING DIRECT AND DIFFUSE INSOLATION ON A HORIZONTAL SURFACE AND PREDICTION OF INSOLATION ON TILTED SURFACES , 1983 .

[8]  Godfried Augenbroe,et al.  Advanced Building Simulation , 2004 .

[9]  Donghyun Seo.  Development of a universal model for predicting hourly solar radiation---Application: Evaluation of an optimal daylighting controller , 2010 .

[10]  François Garde,et al.  Detailed weather data generator for building simulations , 2012, ArXiv.

[11]  Cheol-Yong Jang,et al.  Feasibility study on a novel methodology for short-term real-time energy demand prediction using weather forecasting data , 2013 .

[12]  Barbara Mayer,et al.  Management of hybrid energy supply systems in buildings using mixed-integer model predictive control , 2015 .

[13]  Frauke Oldewurtel,et al.  Building modeling as a crucial part for building predictive control , 2013 .

[14]  Lal Jayamaha.  Energy-Efficient Building Systems: Green Strategies for Operation and Maintenance , 2006 .

[15]  Xinhua Xu,et al.  A supervisory control strategy for building cooling water systems for practical and real time applications , 2008 .