Title :
Performance Evaluation of New and Advanced Neural Networks for Short Term Load Forecasting
Author :
Mehmood, Syed Talha ; El-Hawary, Mohammed
Author_Institution :
Dept. of Electr. & Comput. Eng., Dalhousie Univ., Halifax, NS, Canada
Abstract :
Electric power systems are huge real time energy networks where accurate short term load forecasting (STLF) plays an essential role. This paper 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 of Nova Scotia, 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.
Keywords :
feedforward neural nets; load forecasting; neural net architecture; power engineering computing; STLF; advanced neural network architecture performance evaluation; electric power system; hybrid 3-layered feed forward cascaded NN architecture; short term load forecasting; Artificial neural networks; Computer architecture; Forecasting; Load forecasting; Recurrent neural networks; Temperature distribution; Cascaded neural networks; Hybrid architecture; Recurrent Neural Networks; Short term load forecast; artificial neural networks;
Conference_Titel :
Electrical Power and Energy Conference (EPEC), 2014 IEEE
DOI :
10.1109/EPEC.2014.45
