scholarly journals Short-Term Forecasting of Temperature Driven Electricity Load Using Time Series and Neural Network Model

2014 ◽  
Vol 2 (4) ◽  
pp. 327-331 ◽  
Author(s):  
Nengbao Liu ◽  
Vahan Babushkin ◽  
Afshin Afshari
2021 ◽  
Vol 292 ◽  
pp. 116912
Author(s):  
Rong Wang Ng ◽  
Kasim Mumtaj Begam ◽  
Rajprasad Kumar Rajkumar ◽  
Yee Wan Wong ◽  
Lee Wai Chong

2015 ◽  
Vol 9 (2) ◽  
pp. 202-209 ◽  
Author(s):  
Yanjie Ji ◽  
Phil Blythe ◽  
Weihong Guo ◽  
Wei Wang ◽  
Dounan Tang

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.


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