Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

Badar ul Islam; Zuhairi Baharudin; Perumal Nallagownden

doi:10.4028/www.scientific.net/AMM.785.14

Paper Titles

Comparison of EPSO, PSO and EP Approaches in Transmission Loss Minimization in Power System Network
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Optimum Substation Placement and Feeder Routing Using GA-MST
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Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm
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A Comprehensive Study of Improved Evolutionary Particle Swarm Optimization (IEPSO) for Network Reconfiguration with DGs Sizing Concurrently
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Cuckoo Search Algorithm in Loss Minimization via Capacitor Placement in Distribution System
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Artificial Intelligence Based Technique for the Classification of Incipient Faults in Power Transformer
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Multi-Objective Optimization for Sizing of Distributed Generation Using Cuckoo Search Algorithm
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Application of Artificial Bee Colony Algorithm for Distribution Network Reconfiguration
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 785Short Term Electric Load Forecasting with Back...

Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

Abstract:

Although, Back Propagation Neural Network are frequently implemented to forecast short-term electricity load, however, this training algorithm is criticized for its slow and improper convergence and poor generalization. There is a great need to explore the techniques that can overcome the above mentioned limitations to improve the forecast accuracy. In this paper, an improved BP neural network training algorithm is proposed that hybridizes simulated annealing and genetic algorithm (SA-GA). This hybrid approach leads to the integration of powerful local search capability of simulated annealing and near accurate global search performance of genetic algorithm. The proposed technique has shown better results in terms of load forecast accuracy and faster convergence. ISO New England data for the period of five years is employed to develop a case study that validates the efficacy of the proposed technique.

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Periodical:

Applied Mechanics and Materials (Volume 785)

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14-18

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.785.14

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Online since:

August 2015

Authors:

Badar ul Islam*, Zuhairi Baharudin, Perumal Nallagownden

Keywords:

Back Propagation Neural Network, Genetic Algorithm, Short Term Load Forecasting, Simulated Annealing Algorithm, Weight Adjustment

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