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Author: search.filters.author.Katruksa, Sooppasek

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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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    Electricity load forecasting based on a geographic information system
    (2019-07-01)
    Katruksa, Sooppasek
    ;
    Jiriwibhakorn, Somchat
    This paper continues to develop the previous paper on Application Data for Electricity Load Forecasting Models by applying the method to the geographic information system (GIS) technology in medium-term energy forecasting for the Metropolitan Electricity Authority (MEA) area of Bangkok, Thailand. This method can be employed to improve the electricity load efficiency of the MEA. The spatial prediction plays a key role in the expansion of the areas of electricity distribution, such as the decision-making regarding investment in new substations and power system planning for maintenance and operations. The results appear to indicate that the prediction of the point density of the MEA areas was proportional to the electricity demand in the MEA areas.
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    Application data for electricity load forecasting models
    (2019-07-01)
    Katruksa, Sooppasek
    ;
    Jiriwibhakorn, Somchat
    This paper used artificial neural networks (ANN) in medium-term energy forecasting for the Metropolitan Electricity Authority (MEA) area of Bangkok, Thailand. This method could improve the electricity load efficiency of the MEA. Moreover, The combined ANN with the GIS in next paper have a key role in the decision-making for investment in new substation and power system planning for maintenance and operation. The input data were clustered by K-means algorithms before training by forecasting the models. In this research, the energy forecasting models were the ANN (2 hiddens 4 hiddens). The prediction was based on the MEA's electrical energy history (six months; three months) and the gross domestic product (GDP). The results appeared to indicate that the prediction of ANN 4 hiddens (Classified Data Input) is more accurate.