Short-term load forecasting of Gothatar feeder of Nepal Electricity Authority using Recurrent Neural Network

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dc.contributor.authorAcharya, Sudarshan
dc.date.accessioned2023-11-30T06:44:29Z
dc.date.available2023-11-30T06:44:29Z
dc.date.issued2023-10
dc.descriptionThe RNN method proved to be the accurate and best forecasting method when the results are compared with other forecasting methods in terms of different error measurements i.e., RMSE, R2 and MAPE. Root Mean Square Error (RMSE) of 69.03 kVA, R2 of 0.876 and MAPE of 4.35% obtained from RNN. So, the RNN model proved to be the most accurate and best method with very less error and better R2 in this study.en_US
dc.description.abstractThis paper mainly focuses on short-term forecasting, gives an hourly demand forecast of electricity. Forecasting using Recurrent Neural Network (RNN) is helpful for making important decisions in the field of preventing misbalancing in load and power generation, scheduling, load switching strategies, preventing imbalance in the load demand and power generation, thus leading to greater power quality and network reliability. We use a method called Recurrent Neural Network (RNN) to anticipate the future hourly demand of Gothatar feeder, Nepal Electricity Authority (NEA). The RNN Network is build, trained and test with historical hourly demand data along with six different input variables and used for the prediction of day ahead hourly demand. The output from RNN model is validated with the real hourly demand data collected from NEA.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14540/20765
dc.language.isoenen_US
dc.publisherI.O.E. Pulchowk Campusen_US
dc.subjectNeural Network,en_US
dc.subjectShort-term load forecastingen_US
dc.titleShort-term load forecasting of Gothatar feeder of Nepal Electricity Authority using Recurrent Neural Networken_US
dc.typeThesisen_US
local.academic.levelMastersen_US
local.affiliatedinstitute.titlePulchowk Campusen_US
local.institute.titleInstitute of Engineeringen_US
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