Short term load forecasting in Greek interconnected power system using ANN: A study for output variables

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dc.contributor.author Tsekouras, GJ en
dc.contributor.author Kanellos, FD en
dc.contributor.author Elias, ChN en
dc.contributor.author Kontargyri, VT en
dc.contributor.author Tsirekis, CD en
dc.contributor.author Karanasiou, IS en
dc.contributor.author Salis, AD en
dc.contributor.author Contaxis, PA en
dc.contributor.author Gialketsi, AA en
dc.contributor.author Mastorakis, NE en
dc.date.accessioned 2014-03-01T02:53:28Z
dc.date.available 2014-03-01T02:53:28Z
dc.date.issued 2011 en
dc.identifier.uri http://hdl.handle.net/123456789/36341
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-82955164951&partnerID=40&md5=2c5c9f97f4b1079fae00af599490449e en
dc.subject ANN training scaled conjugate gradient algorithm en
dc.subject Artificial neural networks en
dc.subject Output variables' analysis en
dc.subject Short-term load forecasting en
dc.subject.other Activation functions en
dc.subject.other Artificial Neural Network en
dc.subject.other Calibration process en
dc.subject.other Crucial parameters en
dc.subject.other Different structure en
dc.subject.other Experimental measurements en
dc.subject.other Historical data en
dc.subject.other Hourly load en
dc.subject.other Input variables en
dc.subject.other Load demand en
dc.subject.other Load predictions en
dc.subject.other Mean absolute percentage error en
dc.subject.other Output variables en
dc.subject.other Scaled conjugate gradient algorithm en
dc.subject.other Scaled conjugate gradients en
dc.subject.other Short term load forecasting en
dc.subject.other Short-term forecasting en
dc.subject.other Test sets en
dc.subject.other Training algorithms en
dc.subject.other Algorithms en
dc.subject.other Conjugate gradient method en
dc.subject.other Forecasting en
dc.subject.other Neural networks en
dc.subject.other Power transmission en
dc.subject.other Systems science en
dc.subject.other Electric load forecasting en
dc.title Short term load forecasting in Greek interconnected power system using ANN: A study for output variables en
heal.type conferenceItem en
heal.publicationDate 2011 en
heal.abstract The purpose of this paper is to compare the performance of different structures of Artificial Neural Networks (ANNs) regarding the output variables used for short term forecasting of the next day load of the interconnected Greek power system. In all cases the output variables are the hourly actual loads of the next day. The classical ANN design adopts an ANN model with 24 output variables. Alternatively, 24 different ANN models can be implemented for each hour of the day. This solution can affect the selection of input variables indirectly. In this paper, various scenarios of the solution of 24 different ANN models are going to be studied with different sets of input variables using the scaled conjugate gradient training algorithm, for which a calibration process is conducted regarding the crucial parameters values, such as the number of neurons, the type of activation functions, etc. The performance of each structure is evaluated by the Mean Absolute Percentage Error (MAPE) between the experimental measurements and estimated values of the hourly load demand of the next day for the evaluation set in order to specify the optimal ANN. Next, the load demand for the next day of the test set (with the historical data of the current year) is estimated using the best ANN structure, to verify the behaviour of ANN load prediction techniques. Finally the classical design and different proposed structures are compared. en
heal.journalName Recent Researches in System Science - Proceedings of the 15th WSEAS International Conference on Systems, Part of the 15th WSEAS CSCC Multiconference en
dc.identifier.spage 440 en
dc.identifier.epage 445 en

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