smart energy systems – international conference - forecasting of … · 2019. 4. 18. · smart...
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![Page 1: Smart Energy Systems – international conference - Forecasting of … · 2019. 4. 18. · SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATING Copenhagen, 12‐13 September 2017](https://reader036.vdocument.in/reader036/viewer/2022063020/5fe1eef241082d57952bc823/html5/thumbnails/1.jpg)
Forecasting of heat demand in distr ict heat ing systems and their integrat ion into smart gr id control lers – Fractals,
ensembles and expert advisersC O P E N H A G E N , 1 3 S E P T E M B E R 2 0 1 7D a v y G e y s e n – G o w r i S u r y a n a r a y a n a
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Overview
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
Part I The context: STORMPart II Machine learningPart III Expert AdvicePart IV ResultsPart V Conclusions
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Self‐organising Thermal OperationalResource Management
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017
• 4th generation DHC
• Generic intelligent DHC network controller– Thermal load forecasting
• Karlshamn DHS – 100 buildings
www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
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Overview
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
Part I The context: STORMPart II Machine learningPart III Expert AdvicePart IV ResultsPart V Conclusions
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Machine learning
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
• Building a model from sample inputs
Training & Validation Test
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Supervised Machine learning
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
• Input vector (features) to output value (target)
– Multiple linear regression (LR)
– Decision Tree Learning1• Extremely‐Randomized Trees (ETR)
– Artificial Neural Network (ANN)2: Pierre Geurts, Damien Ernst, and Louis Wehenkel. 2006. Extremely randomized trees. Mach. Learn. 63, 1 (April 2006), 3‐42. DOI=http://dx.doi.org/10.1007/s10994‐006‐6226‐1
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Feature selection
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
• Temperature forecast and historic thermal load
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Feature selection
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
• Timing information
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Feature selection
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
• Temperature forecast + lags– Quadratic and cubic dependence on the above temp features
• Thermal load + lags– Quadratic and cubic dependence on the above thermal load features
• Timing information– Day of the year, day of the week, hour of the day
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Overview
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
Part I The context: STORMPart II Machine learningPart III Expert AdvicePart IV ResultsPart V Conclusions
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Expert advice
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017
• Combine N thermal load forecasting experts– Multiple linear regression, ExtRa Trees regressor, Artificial Neural Network
• Track the best expert– Losses and regret– Minimize
• Fixed‐share forecaster (FS)2
www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
2: P. Gaillard, Y. Goude, Forecasting electricity consumption by aggregating experts; how to design a good set of experts, in: A. Antoniadis, X. Brossat, J.‐M. Poggi (Eds.), Modeling and Stochastic Learning for Forecasting in High Dimensions, Vol. 217 of Lecture Notes in Statistics, Springer, 2015, pp. 95{115. doi:10.1007/978‐3‐319‐18732‐7.
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Expert advice
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
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Overview
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
Part I The context: STORMPart II Machine learningPart III Expert AdvicePart IV ResultsPart V Conclusions
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Results Expert Advice
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
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Results Expert Advice
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
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Results Expert Advice
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
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Results Expert Advice
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
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Overview
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
Part I The context: STORMPart II Machine learningPart III Expert AdvicePart IV ResultsPart V Conclusions
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Conclusions
3rd international conference on SMART ENERGY SYSTEMS AND 4TH GENERATION DISTRICT HEATINGCopenhagen, 12‐13 September 2017www.4dh.eu www.reinvestproject.eu www.heatroadmap.eu
• Robust and generic thermal load forecaster– Easy to add and remove experts– Reduces susceptibility to changes in the DHS– Sufficient training data needed– Python 3.5, scikit‐learn
• Outperforms fractals
• Integration of forecaster in smart controller– Shift peak production to integrate more renewables
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Forecasting of heat demand in distr ict heat ing systems and their integrat ion into smart gr id control lers – Fractals,
ensembles and expert advisersC O P E N H A G E N , 1 3 S E P T E M B E R 2 0 1 7D a v y G e y s e n – G o w r i S u r y a n a r a y a n a