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Titel: Short term load forecasting using hybrid adaptive fuzzy neural system: The performance evaluation
VerfasserIn: Minhas, Daud Mustafa
Khalid, Raja Rehan
Frey, Georg
Sprache: Englisch
Titel: Theme: "Harnessing energy, information and communications technology (ICT) for affordable electrification of Africa" : 2017 IEEE PES‐IAS PowerAfrica Conference : 27-30 June 2017, Accra, Ghana : conference proceedings
Startseite: 468
Endseite: 473
Verlag/Plattform: IEEE
Erscheinungsjahr: 2017
Erscheinungsort: Piscataway
Konferenzort: Accra, Ghana
Freie Schlagwörter: Forecasting
Load modeling
Load forecasting
Linear regression
Predictive models
Temperature distribution
Neural networks
DDC-Sachgruppe: 600 Technik
Dokumenttyp: Konferenzbeitrag (in einem Konferenzband / InProceedings erschienener Beitrag)
Abstract: In this paper, an evaluation theory of hybrid model for short-term electricity load forecasting is presented using simple soft-technique of predicting data. A model that integrates fuzzy system with neural network database is demonstrated and eventually compared with a traditional statistical method of linear regression. Power load forecasting errors especially for weekends, which is much higher than that of weekdays, is reduced using the probabilistic and stochastic natured Hybrid Adaptive Fuzzy Neural System (HAFNS) method. Neural network database uses temperature and power loads as predictors to train the data sets and then use fuzzy system to develop membership functions, forecasting future power load demands for subsequent hours. HAFNS model is made using power load and temperature data of 2015. The training and testing set of HAFNS is composed of yearly data, which may be decomposed on monthly, daily and hourly basis for comparison. The simulation results of the forecasted data including error distribution graphs are demonstrated.
DOI der Erstveröffentlichung: 10.1109/PowerAfrica.2017.7991270
URL der Erstveröffentlichung: https://ieeexplore.ieee.org/document/7991270
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-366266
hdl:20.500.11880/33269
http://dx.doi.org/10.22028/D291-36626
ISBN: 978-1-5090-4746-8
978-1-5090-4747-5
Datum des Eintrags: 5-Jul-2022
Fakultät: NT - Naturwissenschaftlich- Technische Fakultät
Fachrichtung: NT - Systems Engineering
Professur: NT - Prof. Dr. Georg Frey
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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