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Subject: search.filters.subject.Artificial Neural Networks (ANNs)

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    Item type:Publication,
    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.