Similarity Peak Load Forecasting Based on Long Short Term Memory

Ermatita, Ermatita (2022) Similarity Peak Load Forecasting Based on Long Short Term Memory. Turnitin Universitas Sriwijaya. (Submitted)

Text Turnitin - Peak Load Forecasting Based on Long Short Term Memory (1).pdf Download (1MB)

Abstract

Electricity load forecasting is a very imperative issue not only in the power industry but also one of the factors to expand the economic efficiency of power and integral to the plan and execution of various vital. In this case, we use a machine learning approach, exclusively, Long Short Term Memory (LSTM) for predicting future the peak load based on historical data which recorded from Sub-Station in Lhokseumawe, Indonesia. LSTM is capable of forecasting complex univariate electric load time series with strong no stationarity. The result shows the effectiveness of LSTM and outperform traditional forecasting methods in the challenging peak load record problem.

Item Type:	Other
Subjects:	#3 Repository of Lecturer Academic Credit Systems (TPAK) > Results of Ithenticate Plagiarism and Similarity Checker
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Dr Ermatita zuhairi
Date Deposited:	25 Jun 2024 06:08
Last Modified:	25 Jun 2024 06:08
URI:	http://repository.unsri.ac.id/id/eprint/147748

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