000102188 001__ 102188
000102188 005__ 20190331192709.0
000102188 037__ $$aARTICLE
000102188 245__ $$aLoad forecasting with neural nets: prediction of the hourly load with a time horizon of up to seven days-a common project of EOS, EPFL and ABB
000102188 269__ $$a1996
000102188 260__ $$c1996
000102188 336__ $$aJournal Articles
000102188 500__ $$aload forecasting;neural nets;hourly load prediction;EOS;EPFL;ABB;modelling;nonlinear processes;measured loads;seven days ahead prediction;average temperatures;predicted day temperatures;temperature modelling;load modelling;online testing;
000102188 520__ $$aThis article discusses the forecasting of load for a period varying from an hour to a week. As usual for the modelling and prediction of nonlinear processes, the use of artificial neural nets appears very promising. In the project described, measured loads for the past seven days, those for the two preceding days, maxima and minima of average temperatures of the previous day, and predicted day temperatures were used as input values, with the day of the week and day of the year as indicators. The load and temperature are modelled separately. An automated method is used for online testing
000102188 6531_ $$aload (electric);load forecasting;neural nets;power system analysis 	computing;
000102188 700__ $$aBuchenel, B.
000102188 700__ $$0243805$$aGermond, A.$$g105230
000102188 700__ $$aPiras, A.
000102188 700__ $$aJaccard, Y.
000102188 700__ $$aImhof, K.
000102188 700__ $$aBernascon, J.
000102188 700__ $$aDondi, P.
000102188 773__ $$j87$$k21$$q11-16$$tBulletin des schweizerischen elektrotechnischen vereins
000102188 909C0 $$0252145$$pLRE$$xU10310
000102188 909C0 $$0252310$$pEMC$$xU12147
000102188 909CO $$ooai:infoscience.tind.io:102188$$pSTI$$particle
000102188 937__ $$aLRE-ARTICLE-1996-002
000102188 937__ $$aEPFL-ARTICLE-102188
000102188 970__ $$a5461217/LRE
000102188 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000102188 980__ $$aARTICLE