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PERFORMANCE EVALUATION OF NEW AND ADVANCED NEURAL NETWORKS FOR SHORT TERM LOAD FORECASTING: CASE STUDIES FOR MARITIMES AND ONTARIO

Electric power systems are huge real time energy distribution networks where accurate short term load forecasting (STLF) plays an essential role. This thesis is an effort to comprehensively investigate new and advanced neural network (NN) architectures to perform STLF. Two hybrid and two 3-layered NN architectures are introduced. Each network is individually tested to generate weekday and weekend forecasts using data from three jurisdictions of Canada.
Overall findings suggest that 3-layered cascaded NN have outperformed almost all others for weekday forecasts. For weekend forecasts 3-layered feed forward NN produced most accurate results. Recurrent and hybrid networks performed well during peak hours but due to occurrence of constant high error spikes were not able to achieve high accuracy.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/49069
Date02 April 2014
CreatorsMehmood, Syed Talha
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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