Peak Load Forecast using Long Short Term Memory Networks.

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dc.contributor.authorKonda, Ananta Hari
dc.date.accessioned2023-11-30T05:54:31Z
dc.date.available2023-11-30T05:54:31Z
dc.date.issued2023-10
dc.descriptionForecasting is the technique of making scientific predictions of the future based on historical data trends. With increasing smart grid technologies and penetration of intermittent renewable energy technologies, peak load forecasting has been an important task in recent years for optimizing power grid operations.en_US
dc.description.abstractForecasting is the technique of making scientific predictions of the future based on historical data trends. With increasing smart grid technologies and penetration of intermittent renewable energy technologies, peak load forecasting has been an important task in recent years for optimizing power grid operations. In recent years, machine learning has been one of the popular methods for load forecasting. In this paper, a deep learning single Long Short TermMemory (LSTM) based model is proposed for predicting the peak load demand of Nepal and compared with other statistical models. Comparing the evaluation metrics, it is deduced that the proposed LSTM model with 32 LSTM unit and a lookback of 30 has better forecast accuracy with Mean Absolute Error (MAE) of 34.01 MW, Mean Squared Error(MSE) of 2880.75MW, Root Mean Squared Error(RMSE) of 53.67MW, Mean Absolute Percent Error(MAPE) of 2.950% and 𝑅2 Score of 0.933. Among the statistical models considered, theWeighted Moving Average with a loopback of 30 days had the least forecast errors with a Mean Absolute Error(MAE) of 34.77 MW, Mean Squared Error(MSE) of 3271.68 MW, Root Mean Squared Error(RMSE) of 57.19 MW, Mean Absolute Percent Error(MAPE) of 3.005% and 𝑅2 Score of 0.921. 3en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14540/20754
dc.language.isoenen_US
dc.publisherI.O.E. Pulchowk Campusen_US
dc.subjectPeak Load,en_US
dc.subjectForecasting,en_US
dc.subjectNetworks.en_US
dc.titlePeak Load Forecast using Long Short Term Memory Networks.en_US
dc.typeThesisen_US
local.academic.levelMastersen_US
local.affiliatedinstitute.titlePulchowk Campusen_US
local.institute.titleInstitute of Engineeringen_US
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