Please use this identifier to cite or link to this item: https://repository.unej.ac.id/xmlui/handle/123456789/85995
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dc.contributor.authorSantika, Gayatri Dwi-
dc.date.accessioned2018-06-26T02:24:02Z-
dc.date.available2018-06-26T02:24:02Z-
dc.date.issued2018-06-26-
dc.identifier.isbn978-1-5386-0599-8-
dc.identifier.urihttp://repository.unej.ac.id/handle/123456789/85995-
dc.descriptionProceeding The 4th International Conference Computer Applications and information Processing Technology (CAIPT 2017)en_US
dc.description.abstractForecasting of the future load is important because of dramatic changes occurring in the electricity consumption lifestyle. Several algorithms have been suggested for solving this problem. This paper introduces a new modified fuzzy neural system approach for short term load forecasting. By using two phase on Fuzzy Inference system and Genetic algorithm for optimization, weight can improve the accuracy of electricity load forecasting. The relationship external factors like temperature, humidity, price load, Gross Domestic Product and load is identified with a case study for a particular region. Data for a monthly load of five years has been used. The accuracy algorithm has been validated using Root Mean Square Error (RMSE). The result RMSE is 0.78 it is shown that our proposed method is feasible.en_US
dc.language.isoenen_US
dc.subjectElectricity load forecastingen_US
dc.subjecthybriden_US
dc.subjectGenetic Algorithmen_US
dc.subjectFuzzy Neural Systemen_US
dc.subjectexternal factorsen_US
dc.subjectRMSEen_US
dc.titleEnabling External Factors for Consumption Electricity Forecasting using Hybrid Genetic Algorithm and Fuzzy Neural Systemen_US
dc.typeProsidingen_US
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