02956nam 2200757z- 450 991057688340332120231214133138.0(CKB)5720000000008340(oapen)https://directory.doabooks.org/handle/20.500.12854/84505(EXLCZ)99572000000000834020202206d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvanced Methods of Power Load ForecastingBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (128 p.)3-0365-4218-3 3-0365-4217-5 This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load.Research & information: generalbicsscPhysicsbicsscProphet modelHolt–Winters modellong-term forecastingpeak loadprophet modelmultiple seasonalitytime seriesdemandloadforecastDIMSirregulargalvanizingshort-term electrical load forecastingmachine learningdeep learningstatistical analysisparameters tuningCNNLSTMshort-term load forecastArtificial Neural Networkdeep neural networkrecurrent neural networkattentionencoder decoderonline trainingbidirectional long short-term memorymulti-layer stackedneural networkshort-term load forecastingpower systemResearch & information: generalPhysicsGarcía-Díaz J. Carlosedt1323492Trull ÓscaredtGarcía-Díaz J. CarlosothTrull ÓscarothBOOK9910576883403321Advanced Methods of Power Load Forecasting3035623UNINA