000188682 001__ 188682
000188682 005__ 20180913062018.0
000188682 0247_ $$2doi$$a10.1109/SustainIT.2013.6685208
000188682 02470 $$2ISI$$a000330731100022
000188682 037__ $$aCONF
000188682 245__ $$aElectricity Load Forecasting for Residential Customers: Exploiting Aggregation and Correlation between Households
000188682 269__ $$a2013
000188682 260__ $$bIEEE$$c2013
000188682 336__ $$aConference Papers
000188682 520__ $$aThe recent development of smart meters has allowed the analysis of household electricity consumption in real time. Predicting electricity consumption at such very low scales should help to increase the efficiency of distribution networks and energy pricing. However, this is by no means a trivial task since household-level consumption is much more irregular than at the transmission or distribution levels. In this work, we address the problem of improving consumption forecasting by using the statistical relations between consumption series. This is done both at the household and district scales (hundreds of houses), using various machine learning techniques, such as support vector machine for regression (SVR) and multilayer perceptron (MLP). First, we determine which algorithm is best adapted to each scale, then, we try to find leaders among the time series, to help short-term forecasting. We also improve the forecasting for district consumption by clustering houses according to their consumption profiles.
000188682 6531_ $$aload forecasting
000188682 6531_ $$ashort-term forecasting
000188682 6531_ $$asmart grid
000188682 6531_ $$aleaders
000188682 6531_ $$aaggregation
000188682 6531_ $$acorrelation
000188682 6531_ $$aresidential load forecasting
000188682 6531_ $$asubscale
000188682 6531_ $$asmart meter data analytics
000188682 700__ $$0(EPFLAUTH)222974$$aHumeau, Samuel François Roger Joseph$$g222974
000188682 700__ $$0245593$$aWijaya, Tri Kurniawan$$g211617
000188682 700__ $$0247008$$aVasirani, Matteo$$g231501
000188682 700__ $$0240941$$aAberer, Karl$$g134136
000188682 7112_ $$aSustainable Internet and ICT for Sustainability (SustainIT)$$cPalermo, Italy$$dOctober 30-31, 2013
000188682 773__ $$tSustainable Internet and ICT for Sustainability (SustainIT), 2013
000188682 8564_ $$s161004$$uhttps://infoscience.epfl.ch/record/188682/files/rlf_humeau.pdf$$yPostprint$$zPostprint
000188682 909C0 $$0252004$$pLSIR$$xU10405
000188682 909CO $$ooai:infoscience.tind.io:188682$$pconf$$pIC
000188682 917Z8 $$x211617
000188682 917Z8 $$x211617
000188682 917Z8 $$x211617
000188682 917Z8 $$x211617
000188682 917Z8 $$x211617
000188682 917Z8 $$x148230
000188682 937__ $$aEPFL-CONF-188682
000188682 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000188682 980__ $$aCONF