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首页> 外文期刊>Industrial and organizational psychology >Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model
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Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model

机译：使用自适应神经模糊推理系统和粒子群优化 - 人工神经网络模型比较光伏系统的功率输出预测

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摘要

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.
机译：光伏（PV）系统的功率输出预测在决定在泰国纳克·Ratchasima中安装光伏系统之前是必不可少的，由于功率产生不均匀的数据。本研究通过使用自适应神经模糊推理系统（ANFIS）来模拟PV系统的功率输出预测，与人工神经网络方法（PSO-ANN）相结合的粒子群优化的精度。仿真结果表明，与ANFIS方法的预测比PSO-ANN方法更准确。 ANFIS和PSO-ANN模型的性能被均衡为均方误差（MSE），根均方误差（RMSE），平均绝对误差（MAP）和平均百分比误差（MAPE）。 ANFIS模型的准确性为99.8532％，PSO-ANN方法为98.9157％。评估了模型的功率输出预测结果并表明，与现有的电力输出和投资决策的方法相比，所提出的ANFIS预测方法更有益。因此，对PV系统的功率输出的生产分析对于最有利和分析投资成本至关重要。

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