Short-term wind power prediction in microgrids using a hybrid approach integrating genetic algorithm, particle swarm optimization, and adaptive neuro-fuzzy inference systems

2018 ◽  
Vol 13 (11) ◽  
pp. 1561-1567 ◽  
Author(s):  
Dehua Zheng ◽  
Yordanos Kassa Semero ◽  
Jianhua Zhang ◽  
Dan Wei
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Fuzzy Inference Systems ◽  
Power Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Neuro Fuzzy ◽  
Wind Power Prediction ◽  
Inference Systems

scholarly journals Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 351 ◽  
Author(s):  
Promphak Dawan ◽  
Kobsak Sriprapha ◽  
Songkiate Kittisontirak ◽  
Terapong Boonraksa ◽  
Nitikorn Junhuathon ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Output ◽  
Fuzzy Inference ◽  
Photovoltaic System ◽  
Mean Square ◽  
Fuzzy Inference Systems ◽  
Swarm Optimization ◽  
Pv Systems ◽  
Neuro Fuzzy ◽  
Inference Systems

The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.

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