Deep Learning Based Average Current Signal Prediction Using LSTM Network
محل انتشار: نخستین همایش "هوش مصنوعی و فناوری های آینده نگر"
سال انتشار: 1402
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 141
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شناسه ملی سند علمی:
ICAIFT01_008
تاریخ نمایه سازی: 16 بهمن 1402
چکیده مقاله:
One of the challenges faced by power distributioncompanies is the prediction of average current in orderto enable proper planning for sudden increases anddecreases that occur in the sinusoidal current signal.This planning can involve reducing production orstrengthening electrical transformers and otherequipment before reaching their limits, resulting in costsavings in terms of repairs, minimizing industrialequipment failures, and ultimately benefiting thecompany. Recently, in line with the smart gridinitiative, data loggers have been installed in city-levelpower substations to transmit information such asvoltage and current. With this data, which spans onemonth, we have developed a deep learning model usingLong Short-Term Memory (LSTM) networks to predictthe average current for the upcoming week. Through acomparative analysis, we have demonstrated thesuperior performance of our LSTM model incomparison to other neural networks, including MLPand GRU.
کلیدواژه ها:
نویسندگان
Yashar Ishan Agha
Department of Computer Engineering, University of Bojnord, Bojnord, Iran
Vahid Kiani
Department of Computer Engineering, University of Bojnord, Bojnord, Iran