DESIGNING AND AGGREGATING EXPERTS FOR ENERGY DEMAND FORECASTING Yannig Goude EDF R&D Georges Oppenheim UPEM & Paris 11 Pierre Gaillard EDF R&D, HEC Paris-CNRS Gilles Stoltz HEC Paris-CNRS SESO 2014 International Thematic Week “Smart Energy and Stochastic Optimization'' June 23 to 27, 2014
25
Embed
Designing and aggregating experts for energy demand forecasting
Designing and aggregating experts for energy demand forecasting. Yannig Goude EDF R&D Georges Oppenheim UPEM & Paris 11 Pierre Gaillard EDF R&D, HEC Paris-CNRS Gilles Stoltz HEC Paris-CNRS. SESO 2014 International Thematic Week - PowerPoint PPT Presentation
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
DESIGNING AND AGGREGATING EXPERTS FOR ENERGY DEMAND FORECASTING
Yannig Goude EDF R&DGeorges Oppenheim UPEM & Paris 11Pierre Gaillard EDF R&D, HEC Paris-CNRSGilles Stoltz HEC Paris-CNRS
SESO 2014 International Thematic Week“Smart Energy and Stochastic Optimization''
June 23 to 27, 2014
| 2
INDUSTRIAL CHALLENGES
Smart grids More and more « real time » data (ex: linky, 1million meters in 2016) Demand response (new tariffs, real time pricing…) New communication tools with customers (webservice, on-line reporting….)
Renewables energy development A more and more probabilistic context
Opening of the electricity market: Losses/gains of customers
Sensors data: Production/consumption sites Smart home, internet of things
New usages/tariffs: Electric cars Heat pumps, smart phones, battery charge, computers, flat screens…. Demand response, special tariffs (time varying…)
| 3
STATISTICAL CHALLENGES Large scale data sets
Parallelizing statistical algorithms Complex data analysing: heteregonous spatial/temporal sampling, different sources/nature of data
Adaptivity Non-parametric models, fonctional data analysis Model selection, data driven penalty…
Sequential estimation Break detection On-line update, sequential data treatment (data flow, connection to big data) Aggregation with on-line weigths
Multi-scale models Multi-horizon models Multi level data on the grid
Data mining of time series
Large scale simulations Simulation platform, parallel processing
| 4
CONTRIBUTIONS
Large scale data sets GAM parallel processing EDF R&D/IBM simulation platform
Adaptivity GAM models, automatic GAM selection functional data analysis (CLR: curve linear regression, KWF: kernel wavelet fonctional)
Spatio temporal/multi-scale models, complex data « Downscaling » electricity consumption: link INSEE (socio-demographic, census) data to local electricity
consumption (meters, grid data) and meteo data EDF R&D/IBM simulation platform
| 5
LOAD FORECASTING
Electricity consumption is the main entry for optimizing the production units
| 6
ELECTRICITY CONSUMPTION DATA
Trend
Yearly, Weekly, Daily cycles
| 7
ELECTRICITY CONSUMPTION DATA
Meteorological events
Special days
| 8
GAM (GENERALIZED ADDITIVE MODELS)
A good trade-off complexity/adaptivity
Publications Application on load forecasting
• A. Pierrot and Y. Goude, Short-Term Electricity Load Forecasting With Generalized Additive Models Proceedings of ISAP power, pp 593-600, 2011.
• R. Nédellec, J. Cugliari and Y. Goude, GEFCom2012: Electricity Load Forecasting and Backcasting with Semi-Parametric Models, International Journal of Forecasting , 2014, 30, 375 - 381.
GAM « parallel »: BAM (Big Additive Models)• S.N. Wood, Goude, Y. and S. Shaw, Generalized additive models for large datasets, Journal of Royal Statistical
Society-C, 2014.
Adaptive GAM (forgetting factor)• A. Ba, M. Sinn, Y. Goude and P. Pompey, Adaptive Learning of Smoothing Functions: Application to Electricity Load
Forecasting Advances in Neural Information Processing Systems 25, 2012, 2519-2527.
| 9
GAM
| 10
GEFCOM COMPETITION
20 substations on the US grid 11 temperature series hourly data from january 2004 to june 2008 9 weeks to predict : 8 from 2005 to 2006, and the one following the train set (no temperature forecast available)105 teams
Nedellec, R.; Cugliari, J. & Goude, Y.GEFCom2012: Electric load forecasting and backcasting with semi-parametric modelsInternational Journal of Forecasting , 2014, 30, 375 - 381
GEFCOM COMPETITION
| 12
GEFCOM COMPETITION
| 13
CURVE LINEAR REGRESSION
Regressing curves on curves Dimension reduction, SVD of cov(Y,X) , selection with penalised model selection Scale to big data sets (SVD+linear regression)
Publications Application on electricity load forecasting
• H. Cho, Y. Goude, X. Brossat & Q. Yao, Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach Journal of the American Statistical Association, 2013, 108, 7-21.
• Cho, H.; Goude, Y.; Brossat, X. & Yao, Q, Modelling and forecasting daily electricity load using curve linear regressionsubmitted to Lecture Notes in Statistics: Modeling and Stochastic Learning for Forecasting in High Dimension .
Clusturing functional data• H. Cho, Y. Goude, X. Brossat & Q. Yao, Clusturing for curve linear regression, technical report, 2013.
| 14
CURVE LINEAR REGRESSION
| 15
OTHER MODELS
Random forest: a popular machine learning method for classification/regression• Breiman, L., . Random Forests, Machine Learning, 45 (1), 2001.
KWF (Kernel Wavelet Functional): another approach for functional data forecasts• See: Antoniadis, A., Brossat, X., Cugliari, J., Poggi, J., Clustering functional data using wavelets. In: Proceedings of
the Nineteenth International Conference on Computational Statistics(COMPSTAT), 2010.• Antoniadis, A., Paparoditis, E., Sapatinas, T., A functional wavelet–kernel approach for time series prediction.
Journal of the Royal Statistical Society: Series B 68(5), 837–857, 2006.
Temperature
Bank Holiday
Lag Load
<6°C
>6°C
yes
no
>55GW
<55GW
http://luc.devroye.org/BRUCE/brucepics.html
| 16
SEQUENTIAL AGREGATION OF EXPERTS
| 17
SEQUENTIAL AGREGATION OF EXPERTS
| 18
SEQUENTIAL AGREGATION OF EXPERTS
Cesa-Bianchi, N., Lugosi, G.: Prediction, Learning, and Games. Cambridge University Press (2006)
| 19
EXPONENTIALLY WEIGHTED AVERAGE FORECASTER (EWA)
| 20
EXPONENTIATED GRADIENT FORECASTER (EG)
| 21
OTHER ALGORITHMS
Theoretical calibrationWorks well in practice
Gaillard, P., Stoltz, G., van Erven, T.: A second-order bound with excess losses (2014).ArXiv:1402.2044
| 22
APPLICATION ON LOAD FORECASTING
Publications• M. Devaine, P. Gaillard, Y. Goude & G. Stoltz, Forecasting electricity consumption by aggregating specialized experts
- A review of the sequential aggregation of specialized experts, with an application to Slovakian and French country-wide one-day-ahead (half-)hourly predictions Machine Learning, 2013, 90, 231-260.
• Gaillard, P. & Goude, Y., Forecasting electricity consumption by aggregating experts; how to design a good set of experts to appear in Lecture Notes in Statistics: Modeling and Stochastic Learning for Forecasting in High Dimension, 2013.
initial « heterogenous » experts: GAM Kernel Wavelet Functional Curve Linear Regression Random Forest
Designing a set of experts from the original ones: 4 « home made » tricks Bagging: 60 experts Boosting:Boosting: trained on such that performs well
45 experts Specializing: focus on cold/warm days, some periods of the year… 24 experts Time scaling: MD with GAM, ST with the 3 initial experts
| 23
combining
Designing experts
COMBINING FORECASTS
| 24
ANOTHER DATA SET: HEAT DEMAND
| 25
Forecasting methods: Industrial implementation on the way (national, substations, cogeneration central in Poland: 30%
better with GAM) CLR: improve automatic clusturing, forecasting the clusters (HMM)
Combining: publication of the R package OPERA (Online Prediction through ExpeRts Aggregation) coming soon application on other data sets derive probabilistic forecasts from a set of experts