NDLI: Performance comparisons of intelligent load forecasting structures and its application to energy-saving load regulation

Content Provider	Springer Nature Link
Author	Wai, Rong Jong Huang, Yu Chih Chen, Yi Chang Lin, You Wei
Copyright Year	2013
Abstract	This study mainly focuses on the development of intelligent forecasting structures via a similar time method with historical load change rates for the hourly, daily and monthly load forecasting simultaneously based on the basic frameworks of fuzzy neural network (FNN) and particle swarm optimization (PSO). In the regulative aspect of network parameters, conventional back-propagation (BP) and PSO tuning algorithms are used, and varied learning rates are designed in the sense of discrete-time Lyapunov stability theory. The performance comparisons of different intelligent forecasting structures including neural network (NN) structure with BP tuning algorithm (NN-BP), FNN structure with BP tuning algorithm (FNN-BP), FNN structure with BP tuning algorithm and varied learning rates (FNN-BP-V), FNN structure with PSO tuning algorithm (FNN-PSO) and newly-designed adaptive PSO (APSO) structure are verified by numerical simulations. In order to verify the effectiveness of the superior APSO forecasting structure in practical energy-saving load regulation, the load forecasting during every 15 min is also given, and its result is used to manipulate the scheduled unloading control of a real case in Taiwan campus.
Starting Page	1797
Ending Page	1815
Page Count	19
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	17
Issue Number	10
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-02-26
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fuzzy theory Neural network Adaptive particle swarm optimization Load forecasting Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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