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Author: search.filters.author.Wannakam, Khanittha

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    Evaluation of wind energy production using weibull distribution and artificial neural networks
    (2018-08-13)
    Wannakam, Khanittha
    ;
    Jiriwibhakorn, Somchat
    Wind turbine power generation planning requires production estimation. Wind power is uncertain depending on the location, wind speed and wind turbine efficiency. This paper presents a method for evaluating wind energy production using Weibull distribution and Artificial neural networks to compare the data recorded by Promthep Alternative Energy Station, Phuket, Thailand. The results show that wind energy estimation using artificial neural networks produces the most accurate results. Mean Absolute Percentage Error is used to determine the minimum error value. Minimal error of training data is 2.524% and the test data is 3.3041%.
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    Assessment of wind power generation
    (2018-08-13)
    Wannakam, Khanittha
    ;
    Jiriwibhakorn, Somchat
    Wind power generation is an unstable source of renewable energy. Electricity depends on the weather at the installation location of the wind turbine. This paper presents the prediction of wind power by using the Weibull distribution and Adaptive Neuro-Fuzzy Inference System. The lowest error rates from the Adaptive Neuro-Fuzzy Inference System were 2.1912% and 4.5678%. This is the error value of the training data and the test data respectively.
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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.