Development and Applications of Localised Numerical Weather Prediction Models in Building Energy Management
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Development and applications of localised Numerical Weather Prediction models in building energy management Dimitris Lazos A thesis in fulfilment of the requirements for the degree of Doctor of Philosophy School of Photovoltaics and Renewable Energy Engineering Faculty of Engineering August 2016 2 1 COPYRIGHT STATEMENT ‘I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.' Signed ……………………………………………........................... Date ……………………………………………........................... AUTHENTICITY STATEMENT ‘I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format.’ Signed ……………………………………………........................... Date ……………………………………………........................... Acknowledgments This thesis is dedicated to Nick “The Machine”. You may not with us now, but you are not forgotten. Victory or Death, my friend. During the writing of the thesis, Mei’s help was much appreciated and I would like to thank her for being earnest and eager to assist. I would also like to thank Yelena, Maria and Stavros for the inspiration. Last, but not least I would like to express my gratitude to Merlinde and Alistair for their valuable guidance and being extremely understanding and flexible throughout the project. i Contents Acknowledgments ____________________________________________________________________________ i List of Figures _______________________________________________________________________________ vi List of Tables __________________________________________________________________________________ x List of Abbreviations _______________________________________________________________________xii 1 Introduction ________________________________________________________________________________ 1 1.1 Research background _______________________________________________________________ 1 1.2 Thesis outline _____________________________________________________________________________ 3 2 Literature review __________________________________________________________________________ 5 2.1 Chapter outline ___________________________________________________________________________ 5 2.2 Forecasting ________________________________________________________________________________ 6 2.2.1 Background______________________________________________________________________________________ 6 2.2.2 Forecasting techniques _________________________________________________________________________ 7 2.2.2.1 Time-series and regression model forecasting __________________________________________ 7 2.2.2.2 Machine learning forecasting _____________________________________________________________ 8 2.2.2.3 Physical model forecasting _______________________________________________________________ 9 2.2.2.4 Technique comparison __________________________________________________________________ 11 2.2.3 Weather variable forecasting ________________________________________________________________ 13 2.2.3.1 Time series and regression weather forecasting ______________________________________ 14 2.2.3.2 Machine learning weather forecasting _________________________________________________ 15 2.2.3.3 Numerical weather predictions (NWP) ________________________________________________ 16 2.2.3.4 Weather forecasting design considerations ___________________________________________ 24 2.2.4 Load forecasting ______________________________________________________________________________ 27 2.2.4.1 Commercial building loads______________________________________________________________ 27 2.2.4.2 Time series and regression load forecasting __________________________________________ 30 2.2.4.3 Machine learning load forecasting _____________________________________________________ 32 2.2.4.4 Physical model load forecasting ________________________________________________________ 34 2.2.4.5 Hybrid models ___________________________________________________________________________ 36 2.2.4.6 Peak load predictions ___________________________________________________________________ 37 2.2.4.7 Forecasting with Degree Hours & Days ________________________________________________ 40 2.2.4.8 Load forecasting summary ______________________________________________________________ 43 2.2.5 Generation forecasting _______________________________________________________________________ 44 2.3 Building energy management systems _____________________________________________ 46 2.3.1 Model Predictive Control systems ___________________________________________________________ 48 ii 2.3.2 Weather forecasting in BEM _________________________________________________________________ 50 2.3.3 Building conditioning _________________________________________________________________________ 53 2.3.3.1 Dynamic conditioning ___________________________________________________________________ 53 2.3.3.2 Preconditioning __________________________________________________________________________ 55 2.3.3.3 Effects and modelling of thermal mass ________________________________________________ 59 2.3.3.4 Forecasting and effects of occupancy __________________________________________________ 61 2.3.4 Energy generation and storage management _______________________________________________ 63 2.4 Summary and research opportunities ______________________________________________ 65 2.4.1 Inclusion of weather predictions in building energy systems _____________________________ 65 2.4.2 Research gaps _________________________________________________________________________________ 67 3 Short term numerical weather forecasting ________________________________________ 70 3.1 Model outline ___________________________________________________________________________ 70 3.2 Weather prediction model design ___________________________________________________ 72 3.2.1 Data acquisition _______________________________________________________________________________ 72 3.2.2 Prediction model architecture _______________________________________________________________ 73 3.2.3 Base prediction models_______________________________________________________________________ 76 3.2.3.1 Persistence prediction model ___________________________________________________________ 76 3.2.3.2 Numerical predictions in TAPM ________________________________________________________ 77 3.2.4 Hybrid prediction models ____________________________________________________________________ 79 3.2.4.1 Linear regression weighted forecasting (WF) model _________________________________ 79 3.2.4.2 Historical data weighted forecasting (WFS) model ___________________________________ 81 3.2.4.3 ARX prediction model ___________________________________________________________________ 85 3.2.5 Forecast sensitivity to output update intervals_____________________________________________ 86 3.2.6 Correction algorithm for extreme heat events ______________________________________________ 87 3.3 Results ___________________________________________________________________________________ 89 3.3.1 Temperature predictions _____________________________________________________________________ 89 3.3.2 Relative humidity predictions _______________________________________________________________ 93 3.3.3 Wind speed predictions ______________________________________________________________________ 95 3.3.4 Abrupt change predictions ___________________________________________________________________ 96 3.3.5 Extreme heat event predictions _____________________________________________________________ 99 3.3.6 Peak load predictions ________________________________________________________________________ 100 3.3.7 Value of localisation _________________________________________________________________________ 101 3.4 Discussion of the model applicability ______________________________________________ 104 4 Peak load forecasting with an ensemble of weather forecasts ________________ 110 4.1 Ensemble forecasting model design ________________________________________________ 110 4.1.1 Model outline and rationale _________________________________________________________________ 110 iii 4.1.2 Ensemble branch parameters _______________________________________________________________ 111 4.1.3 Model outputs and validation _______________________________________________________________ 114 4.2 Peak load prediction model _________________________________________________________ 116 4.2.1 Potential peak periods _______________________________________________________________________ 116 4.2.2 Detection of significant peak loads _________________________________________________________