The Optimized Comparison of The Gray Model Improved by Posterior-error-test and SVM Modified by Markov Residual Error in The Long-medium Power Load Forecast

2010 ◽  
Vol 5 (5) ◽  
Author(s):  
Zhengang Zhang ◽  
Wei Li
Keyword(s):  
Residual Error ◽  
Gray Model ◽  
Load Forecast ◽  
Power Load

Power Load Forecast Based on Fuzzy BP Neural Networks with Dynamical Estimation of Weights

2020 ◽  
Vol 22 (3) ◽  
pp. 956-969 ◽  
Author(s):  
Quanbo Ge ◽  
Haoyu Jiang ◽  
Meiguang He ◽  
Yani Zhu ◽  
Jianmin Zhang
Keyword(s):  
Neural Networks ◽  
Bp Neural Networks ◽  
Load Forecast ◽  
Power Load

Big Data in Power Load Forecast

2021 ◽  
pp. 37-76
Author(s):  
Valentin A. Boicea
Keyword(s):  
Big Data ◽  
Load Forecast ◽  
Power Load

Long-Term Power Load Forecast Based on Improved BP Neural Network

2010 ◽  
Vol 439-440 ◽  
pp. 848-853
Author(s):  
Shuang Chen Li ◽  
Di Yuan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Setup Time ◽  
Time Sequence ◽  
Bp Algorithm ◽  
Load Forecast ◽  
The Neural Network ◽  
Power Load

This article proposed the improvement BP algorithm which solved the neural network to restrain slow and easy well to fall into the partial minimum the question, through setup time sequence forecast model made the long-term power forecast, and made a comparison with the traditional BP natural network.

Analysis of Generating Capacity Dynamics of the Nigerian Power System between 1963 and 2020

2009 ◽  
Vol 62-64 ◽  
pp. 114-119
Author(s):  
A.O. Melodi
Keyword(s):  
Power System ◽  
Growth Dynamics ◽  
National Goal ◽  
Load Forecast ◽  
Operating Modes ◽  
Power Stations ◽  
Load Growth ◽  
Power Load ◽  
Generating Capacity ◽  
Installed Capacity

A study of the growth dynamics of the installed capacity of the Nigerian Power System from 1963 to date was done. Perspective available generating capacity was estimated based on service life of existing installed capacity. The sufficiency of generating capacity for future normal operating modes of the system was estimated based on a power load growth forecast from 2003 to 2020. An analysis of the results shows that all the installed capacity since 1963 to the late 80s would have expired by 2020 i.e outlived their rated service lives. At present, only about 4.9 GW (80%) of total installed capacity is valid. Considering the national goal of significant economic development and estimated load forecast of over 8 GW by 2020, there is an urgent need to renovate or replace expired generating units besides the planned building of new power stations which have long gestation periods.

A power load forecast approach based on spatial-temporal clustering of load data

2017 ◽  
Vol 30 (23) ◽  
pp. e4386 ◽  
Author(s):  
Wei Zhang ◽  
Gang Mu ◽  
Gangui Yan ◽  
Jun An
Keyword(s):  
Load Forecast ◽  
Temporal Clustering ◽  
Power Load
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