Lee, C.-M.; Ko, C.-N. Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network. Energies2016, 9, 987.
Lee, C.-M.; Ko, C.-N. Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network. Energies 2016, 9, 987.
Lee, C.-M.; Ko, C.-N. Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network. Energies2016, 9, 987.
Lee, C.-M.; Ko, C.-N. Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network. Energies 2016, 9, 987.
Abstract
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 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 Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision as compared to various RBFNNs.
Keywords
short-term load forecasting; radial basis function neural network; support vector regression; particle swarm optimization; adaptive annealing learning algorithm
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
