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Subject: search.filters.subject.Artificial Neural Network (ANN)

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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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    DETECTION OF DRIVER DROWSINESS FROM EEG SIGNALS USING WEARABLE BRAIN SENSING HEADBAND
    (2021-05-31)
    Chan, Khune Satt Nyein
    ;
    Srisurangkul, C.
    ;
    Depaiwa, N.
    ;
    Pangkreung, S.
    Driver drowsiness detection plays an important role in the field of road safety and advanced driver assistance system. Electroencephalogram (EEG) signals are one of the most accurate and reliable indicators of fatigue and drowsiness but in the case of detecting drowsiness, its medical graded measuring system can be intrusive to the driver. The purpose of this research is to test the feasibility and usability of the consumer graded EEG sensor to use in a driver drowsiness detection system. The experiment was carried out by using MUSE S brain sensing headband. Fast Fourier Transform (FFT) method was used to extract features from EEG signals. The extracted feature data are then used to build two classification model, the Support Vector Machine (SVM) and Artificial Neural Network (ANN). The detection of drowsiness is the binary classification task which is to classify between drowsy epochs and alert epochs. In the case of detecting only drowsy epochs, the SVM model detected 82.7% of the drowsy epochs which was better than the ANN model which can only detect 81.25% of the drowsy epochs. But in the detection of both drowsy and alert epochs, the ANN model performed better than that of SVM. The SVM model was tested with different kernel function and Fine Gaussian SVM model showed the highest accuracy of 87.8%. The ANN model performed slightly higher than the SVM model with an accuracy of 87.9%. The ability of consumer graded EEG sensor to use in drowsiness detection system was validated in this research.