LAPSE:2019.0292
Published Article
LAPSE:2019.0292
Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network
February 27, 2019
A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using an SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from the Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision compared to various RBFNNs.
Record ID
Keywords
adaptive annealing learning algorithm, Particle Swarm Optimization, radial basis function neural network, short-term load forecasting, support vector regression
Subject
Suggested Citation
Lee CM, Ko CN. Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network. (2019). LAPSE:2019.0292
Author Affiliations
Journal Name
Energies
Volume
9
Issue
12
Article Number
E987
Year
2016
Publication Date
2016-11-25
Published Version
ISSN
1996-1073
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Original Submission
Other Meta
PII: en9120987, Publication Type: Journal Article
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Published Article
LAPSE:2019.0292
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External Link
doi:10.3390/en9120987
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[v1] (Original Submission)
Feb 27, 2019
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Feb 27, 2019
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v1
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https://psecommunity.org/LAPSE:2019.0292
Original Submitter
Calvin Tsay
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