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Open AccessArticle

Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network

1
Department of Digital Living Innovation, Nan Kai University of Technology, Tsaotun, Nantou 542, Taiwan
2
Department of Automation Engineering, Nan Kai University of Technology, Tsaotun, Nantou 542, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Javier Contreras
Energies 2016, 9(12), 987; https://doi.org/10.3390/en9120987
Received: 28 September 2016 / Revised: 11 November 2016 / Accepted: 18 November 2016 / Published: 25 November 2016
(This article belongs to the Special Issue Forecasting Models of Electricity Prices)
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. View Full-Text
Keywords: short-term load forecasting; radial basis function neural network; support vector regression; particle swarm optimization; adaptive annealing learning algorithm short-term load forecasting; radial basis function neural network; support vector regression; particle swarm optimization; adaptive annealing learning algorithm
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Lee, C.-M.; Ko, C.-N. Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network. Energies 2016, 9, 987.

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