scholarly journals Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

Energies ◽  
2013 ◽  
Vol 6 (9) ◽  
pp. 4879-4896 ◽  
Author(s):  
Wen-Yeau Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Power ◽  
Hybrid Method ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Power Forecasting ◽  
Enhanced Particle Swarm Optimization ◽  
Power Forecasting

scholarly journals Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization

2021 ◽  
Vol 15 (2) ◽  
pp. 111-138
Author(s):  
Pavan Kumar Singh ◽  
Nitin Singh ◽  
Richa Negi
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Power ◽  
Smart Grids ◽  
Likelihood Function ◽  
Arima Model ◽  
Particle Swarm ◽  
Model Estimation ◽  
Swarm Optimization ◽  
Wind Power Forecasting ◽  
Power Forecasting

With the upsurge in restructuring of the power markets, wind power has become one of the key factors in power generation in the smart grids and gained momentum in the recent years. The accurate wind power forecasting is highly desirable for reduction of the reserve capability, enhancement in penetration of the wind power, stability and economic operation of the power system. The time series models are extensively used for the wind power forecasting. The model estimation in the ARIMA model is usually accomplished by maximizing the log likelihood function and it requires to be re-estimated for any change in input value. This degrades the performance of the ARIMA model. In the proposed work, the model estimation of the ARIMA model is done using latest evolutionary algorithm (i.e., dynamic particle swarm optimization [DPSO]). The use of DPSO algorithm eliminates the need for re-estimation of the model coefficients for any change in input value and moreover, it improves the performance of ARIMA model. The performance of proposed DPSO-ARIMA model has been compared to the existing models.

scholarly journals Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

A High-Accuracy Hybrid Method for Short-Term Wind Power Forecasting

Energy ◽  
2021 ◽  
pp. 122020
Author(s):  
Sahra Khazaei ◽  
Mehdi Ehsan ◽  
Soodabeh Soleymani ◽  
Hosein Mohammadnezhad-Shourkaei
Keyword(s):  
Wind Power ◽  
Hybrid Method ◽  
High Accuracy ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Power Forecasting
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