Next Article in Journal
The Electric Bicycle: Worldwide Research Trends
Next Article in Special Issue
Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method
Previous Article in Journal
Assimilation of Optimal Sized Hybrid Photovoltaic-Biomass System by Dragonfly Algorithm with Grid
Previous Article in Special Issue
Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting

1
LMO, University Paris-Sud, 91405 Orsay, France
2
ERIC EA 3083, University de Lyon, Lyon 2, 69676 Bron, France
3
EDF R & D, LMO, Univ Paris-Sud, 91405 Orsay, France
4
University Paris Descartes & LMO, Univ. Paris-Sud, 91405 Orsay, France
*
Author to whom correspondence should be addressed.
Energies 2018, 11(7), 1893; https://doi.org/10.3390/en11071893
Received: 29 June 2018 / Revised: 12 July 2018 / Accepted: 16 July 2018 / Published: 20 July 2018
(This article belongs to the Special Issue Short-Term Load Forecasting by Artificial Intelligent Technologies)
  |  
PDF [1063 KB, uploaded 20 July 2018]
  |  

Abstract

Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The first section is dedicated to the industrial context and a review of individual electrical data analysis. Then, we focus on hierarchical time-series for bottom-up forecasting. The idea is to decompose the global signal and obtain disaggregated forecasts in such a way that their sum enhances the prediction. This is done in three steps: identify a rather large number of super-consumers by clustering their energy profiles, generate a hierarchy of nested partitions and choose the one that minimize a prediction criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy gives a 16% improvement in forecasting accuracy when applied to French individual consumers. Then, this strategy is implemented using R—the free software environment for statistical computing—so that it can scale when dealing with massive datasets. The proposed solution is to make the algorithm scalable combine data storage, parallel computing and double clustering step to define the super-consumers. The resulting software is openly available. View Full-Text
Keywords: clustering; forecasting; hierarchical time-series; individual electrical consumers; scalable; short term; smart meters; wavelets clustering; forecasting; hierarchical time-series; individual electrical consumers; scalable; short term; smart meters; wavelets
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed
Printed Edition Available!
A printed edition of this Special Issue is available here.

Share & Cite This Article

MDPI and ACS Style

Auder, B.; Cugliari, J.; Goude, Y.; Poggi, J.-M. Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting. Energies 2018, 11, 1893.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top