Application of PSO algorithm in short-term optimization of reservoir operation

Short-term traffic flow forecasting has a high requirement for the responding time and accuracy of the forecasting method because the result is directly used for instant traffic inducing. Based on the introduction of the fuzzy neural network model for short-term traffic flow forecasting together with its detailed procedures, this paper adopt the particle swarm optimization algorithm to train the fuzzy neural network. Its global searching and optimization algorithm helps to overcome the shortcomings of the traditional fuzzy neural network, such as its low efficiency and “local optimum”. A case study is also given for the PSO algorithm to train the fuzzy neural network for traffic flow forecasting. The result shows that the average square error is 0.932 when the PSO algorithm is put to use for the network training, which is 3.926 when the PSO is not used. Thus result is more accurate and it requires less time for the training procedures. It proves this method is feasible and efficient.

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A short-term reservoir operation model for multicrop irrigation

Hydrological Sciences Journal ◽

10.1080/02626669809492139 ◽

1998 ◽

Vol 43 (3) ◽

pp. 479-494 ◽

Cited By ~ 10

Author(s):

P. P. MUJUMDAR ◽

RAMESH TEEGAVARAPU

Keyword(s):

Reservoir Operation ◽

Operation Model ◽

Short Term

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Application of the Combination Prediction Model in Forecasting the Short-Term Wind Power

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.733.893 ◽

2015 ◽

Vol 733 ◽

pp. 893-897

Author(s):

Peng Yu Zhang

Keyword(s):

Neural Network ◽

Prediction Model ◽

Wind Power ◽

Pso Algorithm ◽

Support Vector ◽

Generalized Regression Neural Network ◽

Power Prediction ◽

Short Term ◽

Combination Forecasting ◽

Wind Power Prediction

The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

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Modeling the Dynamics and Forecasting the fourth Peak of COVID-19 in Iran Using PSO Algorithm

International journal of epidemiologic research ◽

10.34172/ijer.2021.26 ◽

2021 ◽

Vol 8 (4) ◽

pp. 140-145

Author(s):

Ebrahim Sahafizadeh ◽

Mohammad Ali Khajeian

Keyword(s):

Pso Algorithm ◽

Future Trend ◽

Short Term ◽

Estimated Parameters ◽

Term Prediction ◽

Proposed Model ◽

Spread Model ◽

Short Term Prediction ◽

Reported Data ◽

The Given

Background and aims: Iran had passed the third peak of COVID-19 pandemic, and was probably witnessing the fourth peak at the time of this study. This study aimed to model the spread of COVID-19 in Iran in order to predict the short-term future trend of COVID-19 from April 23, 2021 to May 7, 2021. Methods: In this study, a modified SEIR epidemic spread model was proposed and the data on the number of cases reported by Iranian government from February 20, 2020 to April 23, 2021 were used to fit the proposed model to the reported data using particle swarm optimization (PSO) algorithm. Then the short-term future trend of COVID-19 cases were predicted by using the estimated parameters. Results: The results indicated that the effective reproduction number increased in Nowruz (i.e., Persian New Year, 1400) and it was estimated to be 1.28 in the given period. According to the results from the short-term prediction of COVID-19 cases, the number of active confirmed cases in the fourth peak was estimated to be 516411 cases on May 2, 2021. Conclusion: Following the results from our short-term prediction, implementing strict social distancing policies was found absolutely necessary for relieving the Iran’s health care system of the tremendous burden of COVID-19.

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Support vector machine with PSO algorithm in short-term load forecasting

2008 Chinese Control and Decision Conference ◽

10.1109/ccdc.2008.4597492 ◽

2008 ◽

Author(s):

Gao Rong ◽

Liu xiaohua

Keyword(s):

Support Vector Machine ◽

Load Forecasting ◽

Pso Algorithm ◽

Support Vector ◽

Short Term ◽

Short Term Load Forecasting

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