Abstract
Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.
Highlights
Load forecasting is an effective and crucial process in the management and operation of power systems which can lead to significant cost savings when accurately calculated
Electrical load forecasting affected the power system operation and planning processes in a way where the correct operation of the power system depends on precision of this prediction
Exploiting algorithms such as Particle Swarm Optimization (PSO) algorithm can be helped. is paper proposes an approach for electrical load forecasting using PSO algorithm and neural network with backpropagation algorithm
Summary
Load forecasting is an effective and crucial process in the management and operation of power systems which can lead to significant cost savings when accurately calculated. Many intelligent methods such as PSO have been proposed to improve artificial neural networks’ training and architecture in short-. E EMD is used to decompose the load time series and deep neural network is used to perform short-term load forecasting. Reference [18] proposed a deep learning framework based on a combination of a convolutional neural network (CNN) [19] and long short-term memory (LSTM) [20]. Reference [24] proposes a full wavelet neural network method for short-term load forecasting. A combined ultra-short-term load forecasting model for industrial power users is introduced. E grey neural network is used to integrate the two algorithms, which further improves the accuracy of ultra-short-term load forecasting.
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