Short-term wind power forecasting based on Elman neural network with particle swarm optimization

2016 ◽  
Author(s):  
Lei Xu ◽  
Jiandong Mao
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Particle Swarm ◽  
Short Term ◽  
Elman Neural Network ◽  
Swarm Optimization ◽  
Wind Power Forecasting ◽  
Power Forecasting

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.

scholarly journals Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource

2020 ◽  
Vol 10 (4) ◽  
pp. 1295 ◽  
Author(s):  
Bin Tang ◽  
Yan Chen ◽  
Qin Chen ◽  
Mengxing Su
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Wind Power ◽  
Meteorological Data ◽  
Prediction Method ◽  
Short Term ◽  
Elman Neural Network ◽  
Wind Power Forecasting ◽  
Interval Prediction ◽  
Power Forecasting

In order to enhance the accuracy of short-term wind power forecasting (WPF), a short-term wind power forecasting method based on historical wind resources by data mining has been designed. Firstly, the spoiled data resulting from wind turbine and meteorological monitoring equipment is eliminated, and the missing data is added by the Lomnaofski optimization model, which is based on the temporal-spatial correlation of meteorological data. Secondly, the wind characteristics are analyzed by the continuous time similarity clustering (CTSC) method, which is used to select similar samples. To improve the accuracy of deterministic prediction and prediction error, the radial basis function neural network (RBF) deterministic forecasting model was built, which can approximate nonlinear solutions. In addition, the wind power interval prediction method, combining fuzzy information granulation and an Elman neural network (FIG-Elman), is proposed to acquire forecasting intervals. The deterministic prediction of the RBF-CTSC model has high accuracy, which can accurately describe the randomness, fluctuation and nonlinear characteristics of wind speed. Additionally, the mean absolute error (MAE) and root mean square error (RMSE) are reduced by the new model. The interval prediction of FIG-Elman results show that the interval width decreased by 18.85%, and the coverage probability of interval increased by 10.94%.

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 Deterministic and Probabilistic Wind Power Forecasting Based on Bi-Level Convolutional Neural Network and Particle Swarm Optimization

2019 ◽  
Vol 9 (9) ◽  
pp. 1794 ◽  
Author(s):  
Yang ◽  
Zhang ◽  
Yang ◽  
Lv
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Wind Power ◽  
Particle Swarm ◽  
Power Prediction ◽  
Swarm Optimization ◽  
Wind Power Forecasting ◽  
Interval Prediction ◽  
Wind Power Prediction

The intermittency and uncertainty of wind power result in challenges for large-scale wind power integration. Accurate wind power prediction is becoming increasingly important for power system planning and operation. In this paper, a probabilistic interval prediction method for wind power based on deep learning and particle swarm optimization (PSO) is proposed. Variational mode decomposition (VMD) and phase space reconstruction are used to pre-process the original wind power data to obtain additional details and uncover hidden information in the data. Subsequently, a bi-level convolutional neural network is used to learn nonlinear features in the pre-processed wind power data for wind power forecasting. PSO is used to determine the uncertainty of the point-based wind power prediction and to obtain the probabilistic prediction interval of the wind power. Wind power data from a Chinese wind farm and modeled wind power data provided by the United States Renewable Energy Laboratory are used to conduct extensive tests of the proposed method. The results show that the proposed method has competitive advantages for the point-based and probabilistic interval prediction of wind power.

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