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Subject: search.filters.subject.Adaptive Neuro-Fuzzy Inference System (ANFIS)

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    Advanced Short-Term Wind Power Forecasting Based on Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network
    (2025-01-01)
    Huang, Zhibin
    ;
    Jiriwibhakorn, Somchat
    Accurate short-term wind power forecasting plays a critical role in maintaining grid stability and enhancing the efficient utilization of renewable energy, particularly as wind energy continues to contribute increasingly to global electricity generation. This study explores and analyzes two forecasting approaches—Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), aiming to improve predictive accuracy. Both models utilize identical historical wind farm datasets and are trained, tested, and validated using the MATLAB R2023b platform. The research findings demonstrate that both ANN and ANFIS are well-suited for short-term wind power forecasting; however, ANFIS exhibits superior predictive accuracy compared to ANN. Specifically, the coefficient of determination (R<sup>2</sup>) values for ANN and ANFIS are 0.973 and 0.985, respectively. In terms of Root Mean Square Error (RMSE), ANN records 7.82e-03 during training and 7.44e-03 during testing, whereas ANFIS achieves a significantly lower 2.14e-03 in both phases. These results indicate that both models demonstrate a strong fit to actual data, with R² values approaching 1, validating their reliability for short-term forecasting. Furthermore, ANFIS proves to be more effective in handling data nonlinearity and uncertainty, consistently yielding lower RMSE values in both the training and testing phases. Despite achieving higher predictive accuracy, ANFIS requires a longer computational time. While this study confirms ANFIS's superior performance in short-term wind power forecasting, its advantage over ANN is not guaranteed in all scenarios, as the effectiveness of the model remains dependent on the complexity of input data and the choice of training function.
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    Evaluation of mid-term load forecasting case study based on adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs)
    (2020-07-01)
    Katruksa, Sooppasek
    ;
    Jiriwibhakorn, Somchat
    This paper presents the different techniques for Thailand's medium-term load forecasting. It expected that load forecasting can effectively improve the electrical load efficiency of the Electricity Generating Authority of Thailand (EGAT). In addition, the accuracy of load forecasting is an important part of decision-making for power plant investment and the planning of the power distribution system. In this study, the input data has been trained by several predictive models, which have been artificial neural networks (2,3 and 4 hidden layers) and adaptive neuro-fuzzy inference systems. Learning and prediction depends on three important factors, including Thailand's peak load history (simple moving average of 12 months, 9 months, 6 months and 3 months), month codes and Quarterly Gross Domestic Product (QGDP). The results show that training ANN with two hidden layers produces the best predictive performance. The most accurate load forecast using this method is 1.1527% of MAPE and 14.45 minutes of learning time.
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    Evaluation of generation system reliability using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNS)
    (2018-01-01)
    Wannakam, Khanittha
    ;
    Jiriwibhakorn, Somchat
    This paper presents an evaluation of the reliability index of power generation systems using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) to compare the results obtained from the basic method of probability. The reliability index used in this study is the Expected Energy Not Supplied (EENS) index, which is used in planning to increase the installed capacity for the adequate demand for electricity. The ANFIS and ANNs techniques will learn the relationship between the priority level, the installed capacity and the force outage rate (FOR) of the generator, which significantly affect the EENS index. The results indicated that the ANNs techniques have the best predictive performance. The best accuracy of the training data was 1.2488% and the testing data was 2.3963%, calculated using a Mean Absolute Percentage Error (MAPE). Furthermore, the ANNs took more time to learn faster than the ANFIS.