Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

نویسندگان

چکیده

Electrical load forecasting plays a key role in power system planning and operation procedures. So far, variety of techniques have been employed for electrical forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due their ability adapt properly the consuming load's hidden characteristic. Therefore, these were widely accepted by researchers. As parameters neural network significant impact on its performance, this paper, short-term method using particle swarm optimization (PSO) algorithm is proposed, which some including learning rate number layers are determined order forecast PSO precisely. Then, with optimized used predict load. In method, three-layer feedforward trained backpropagation beside an improved gbest algorithm. Also, error defined as cost function. The proposed approach has tested Iranian grid MATLAB software. average three indices graphical results considered evaluate performance method. simulation reflect capabilities accurately predicting

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ژورنال

عنوان ژورنال: Mathematical Problems in Engineering

سال: 2021

ISSN: ['1026-7077', '1563-5147', '1024-123X']

DOI: https://doi.org/10.1155/2021/5598267