Particle Swarm Optimization Based GM(1,2) Method on Day-Ahead Electricity Price Forecasting with Predicted Error Improvement

2010 ◽  
Author(s):  
Ruiqing Wang ◽  
Fuxiong Wang ◽  
Wentian Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Electricity Price ◽  
Price Forecasting ◽  
Swarm Optimization ◽  
Electricity Price Forecasting

Particle Swarm Optimization Based GM(1,1) Method on Short-Term Electricity Price Forecasting with Predicted Error Improvement

2010 ◽  
Vol 40-41 ◽  
pp. 183-188
Author(s):  
Rui Qing Wang ◽  
Fu Xiong Wang ◽  
Wen Tian Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Electricity Market ◽  
Particle Swarm ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Swarm Optimization ◽  
Forecasting Accuracy ◽  
Market Participants ◽  
Electricity Price Forecasting

Under deregulated environment, accurate electricity price forecasting is a crucial issue concerned by all market participants. Experience shows that single forecasting model is very difficult to improve the forecasting accuracy due to the complicated factors affecting electricity prices. In this paper, a particle swarm optimization based GM(1,1) method on short-term electricity price forecasting with predicted error improvement is proposed, in which the moving average method is used to process the raw data, the particle swarm optimization based GM(1,1) model is used to the processed series, and the time series analysis is used to further improve the predicted errors. The numerical example based on the historical data of the PJM market shows that the method can reflect the characteristics of electricity price better and the forecasting accuracy can be improved virtually compared with the conventional GM(1,1) model. The forecasted prices accurate enough to be used by electricity market participants to prepare their bidding strategies.

Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6514
Author(s):  
Min Yi ◽  
Wei Xie ◽  
Li Mo
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Electricity Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Short Term ◽  
Swarm Optimization ◽  
Electricity Price Forecasting ◽  
Bp Model

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting.

scholarly journals A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

2014 ◽  
Vol 2014 ◽  
pp. 1-31 ◽  
Author(s):  
Zhilong Wang ◽  
Feng Liu ◽  
Jie Wu ◽  
Jianzhou Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Electricity Market ◽  
Particle Swarm ◽  
Electricity Price ◽  
Swarm Optimization ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Backpropagation Artificial Neural Network

In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO), the backpropagation artificial neural network (BPANN), and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN) method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.

scholarly journals Stock Price forecasting based on Quantum Particle swarm optimization

2021 ◽  
Vol 04 (10) ◽  
Author(s):  
Guohui Huang ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Stock Price ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Price Forecasting ◽  
Swarm Optimization ◽  
Stock Price Forecasting ◽  
Quantum Particle Swarm Optimization ◽  
Bee Colony

The stock market is very volatile, so the change of the stock price is also widely concerned by investors. In this paper, a new stock price forecasting model based on Quantum Particle Swarm Optimization(QPSO) , Quantum Bee Colony Optimization Algorithm(QABC) and Quantum Fruit Fly Optimization Algorithm (QFOA) is proposed. The three methods all use BP neural network to adjust the parameters of particle swarm, bee colony and Drosophila to reach the optimal parameters. Taking the daily closing price of CITIC Securities and Tianfeng Securities, a large-scale and a small-scale securities company, as the object of empirical analysis, comparing the accuracy of the three methods in predicting stocks, it also analyzes whether the size of the company has an effect on the accuracy of the model. The results show that the prediction effect of qpso is the best, and the size of the company has some influence on the prediction effect.

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