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  4. Cluster-based Aggregate Forecasting for Residential Electricity Demand using Smart Meter Data
 
conference paper

Cluster-based Aggregate Forecasting for Residential Electricity Demand using Smart Meter Data

Wijaya, Tri Kurniawan  
•
Vasirani, Matteo  
•
Humeau, Samuel
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2015
2015 IEEE International Conference on Big Data (Big Data)
IEEE International Conference on Big Data

While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Since electricity consumption behavior may vary between households, we first build a feature universe, and then apply Correlation-based Feature Selection to select features relevant to each household. Additionally, smart meter data can be used to obtain aggregate forecasts with higher accuracy using the so-called Cluster-based Aggregate Forecasting (CBAF) strategy, i.e., by first clustering the households, forecasting the clusters' energy consumption separately, and finally aggregating the forecasts. We found that the improvement provided by CBAF depends not only on the number of clusters, but also more importantly on the size of the customer base.

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Type
conference paper
DOI
10.1109/BigData.2015.7363836
Author(s)
Wijaya, Tri Kurniawan  
Vasirani, Matteo  
Humeau, Samuel
Aberer, Karl  
Date Issued

2015

Published in
2015 IEEE International Conference on Big Data (Big Data)
Start page

879

End page

887

Subjects

electricity load forecasting

•

clustering

•

machine learning

•

smart meter

•

linear regression

•

support vector regression

•

multi-layer perceptron

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LSIR  
Event nameEvent placeEvent date
IEEE International Conference on Big Data

Santa Clara, CA, USA

October 29 - November 1, 2015

Available on Infoscience
October 25, 2015
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/120075
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