The Synergy of Simulation and Time Series Forecasting for Live Performance Testing of Smart Buildings

Differences in requirements for reliability in buildings imply the different needs for calculation of expected building behaviour. In this paper we examine four techniques for calculating expected behaviour of buildings. Two of them are simulation techniques, namely, a white box EnergyPlus model and a æ static tool as per the requirements of the Danish government. The other two are machine learning techniques, namely an ARIMA model, and an long short-term memory artificial recurrent neural network, used in deep learning. We compare and contrast these four techniques based on their accuracy of forecast, as well as execution time to forecast a new data point. Furthermore, we provide an algorithm for selection of forecasting technique based on terms such as availability, accuracy, and execution time requirements, to facilitate real time threshold generation in light of building performance testing.

[1]  François Chollet,et al.  Keras: The Python Deep Learning library , 2018 .

[2]  Sanja Lazarova-Molnar,et al.  Towards smart buildings performance testing as a service , 2018, 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC).

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

[4]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[5]  James E. Hill.  Editorial: Synergy of HVAC and Fire Research , 2005 .

[6]  Hamid Reza Shaker,et al.  Online energy simulator for building fault detection and diagnostics using dynamic energy performance model , 2018 .

[7]  Jin Wen,et al.  Review of building energy modeling for control and operation , 2014 .

[8]  Bo Nørregaard Jørgensen,et al.  Continuous Commissioning of Buildings: A Case Study of a Campus Building in Denmark , 2016, 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[9]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[10]  Alberto Hernandez Neto,et al.  Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .

[11]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[12]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[13]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

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

[15]  Nelson Fumo,et al.  Methodology to estimate building energy consumption using EnergyPlus Benchmark Models , 2010 .

[16]  Elena Markoska,et al.  LEAF: Live Building Performance Evaluation Framework , 2019, 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC).

[17]  Michael Wetter,et al.  A framework for simulation-based real-time whole building performance assessment , 2012 .

[18]  Sanja Lazarova-Molnar,et al.  A Framework for Fully Automated Performance Testing for Smart Buildings , 2018 .