bENBIS: Energy Demand Forecasting Workshop

18 januari 2018 - 13:00 tot 17:00
Kasteelpark Arenberg 1, 3001 Leuven

Tijdens deze workshop maken deelnemers kennis met een breed gamma aan methodes die gebruikt kunnen worden voor energievraagvoorspelling. Het covert zowel methodologische als praktische aspecten die relevant zijn voor een breed publiek van academici tot industrie. De workshop start met een breed overzicht van voorspellingsmethodes voor niet-stationaire tijdreeksen, en past deze toe in het veld van energievraag. Na deze key note lezing, volgen drie specifieke talks die u door verschillende initiatieven en case studies zal gidsen, met aandacht voor praktische belemmeringen en voor- of nadelen van de gebruikte methodes.


  • 13u00 - 13u30: Welkomstdrink
  • 13u30 - 15u00: Spreker 1 - J.M. Poggi (Univ. Paris Descartes & Univ. Paris-Sud Orsay, LMO)  
  • 15u00 - 15u30: Joffiepauze
  • 15u30 - 16u00: Spreker 2 - G. Deconinck (KU Leuven, EnergyVille)
  • 16u00 - 16u30: Spreker 3 - D. Metten (EDF Luminus)
  • 16u30 - 17u00: Spreker 4 - C. Ritter (Ritter en Danielson Consulting & Université Catholique de Louvain)

Datum: 18 januari 2018
Timing: 13u-17u
Locatie: Kasteelpark Arenberg 1, 3001 Heverlee, aula 01.07 map foto
Parking:  P.01 Celestijnenlaan 3001 Heverlee info
Meer info:  Bart De Ketelaere, E. Buyse

​Registreren kan gratis, maar is wel verplicht
​Registreren kan hier.


Titels & Abstracts:

Nonstationary time series forecasting and functional clustering using wavelets. Application to electricity demand.

Jean-Michel Poggi (Univ. Paris Descartes and Univ. Paris-Sud Orsay, LMO).

The talk starts with the industrial motivation of this work about nonparametric forecasting of electricity demand from the point of view the context this collaboration between academia and EDF and presenting methods previously considered in this context. Of course, the methods presented in this talk are quite general but the given application context allows to better highlight pros and cons. We then present two methods for detecting patterns and clusters in high dimensional time-dependent functional data. Our methods are based on wavelet-based similarity measures, since wavelets are well suited for identifying highly discriminant time-scale features. The multiresolution aspect of the wavelet transform provides a time-scale decomposition of the signals allowing to visualize and to cluster the functional data into homogeneous groups. For each input function, through its empirical orthogonal wavelet transform the first method uses the distribution of energy across scales to generate a representation that can be sufficient to make the signals well distinguishable. Our new similarity measure combined with a feature selection technique is then used within classical clustering algorithms to effectively differentiate among high dimensional populations. The second method uses similarity measures between the whole time-scale representations that are based on wavelet-coherence tools. The clustering is then performed using a k-centroid algorithm starting from these similarities. Finally, the practical performance of these methods is illustrated through the daily profiles of the French electricity power demand involved in nonparametric forecasting as well as individual consumers clustering involved in the forecasting by disaggregation of the electricity consumption.

Scalable data-driven modeling for demand response in smart grids 

Geert Deconinck (Afdeling ESAT - ELECTA, Elektrische Energie en Computerarchitecturen, KU Leuven)

Smart grids require lots of data to characterise the flexibility in electricity demand. Demand response (e.g. shifting appliance use) allows to integrate more renewables into the grid, or to have lower energy costs, without impacting user comfort. Within EnergyVille, KU Leuven's research centre on sustainable energy for an urban environment, a cloud-based data platform has been developed to capture and process such data - together with data from energy markets and users - in order to use it as a basis for scalable demand response applications. This presentation will shed a light on such applications and on the underlying platform.

Impact of embedded solar generation on energy consumption profiles

David Metten (EDF Luminus)

Production of solar panels on the roofs of residential customers has a significant impact on the energy consumption profiles of these customers. Presentation will give a view on the problems we faced, analysis we’ve done and solutions we came up with to challenge this profile behavior.

About electric elephants, viking ships, and the dunes of gas 

Christian Ritter (Ritter and Danielson Consulting and Université Catholique de Louvain)

Everybody consumes energy in their own way; yet, on average there are just a few characteristic patterns. This talk is about these patterns, what they look like and why, and about what some of this means for the Belgian energy markets. It is an entry level presentation focusing on graphics.


Deel dit bericht
Deel dit bericht