Corresponding Author : Short Term Electrical Energy Consumption Forecasting using RNN-LSTM

Nurmaini, Siti and Suprapto, Bhakti Yudho (2019) Corresponding Author : Short Term Electrical Energy Consumption Forecasting using RNN-LSTM. Universitas Sriwijaya - IEEE.

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Abstract

The estimation of a-day forward short-term electrical consumption in this research is using time series daily data from PT Perusahaan Listrik Negara (PLN) in 5-year periods. This research proposed Recurrent Neural Network and Long Shot Term Memory (LSTM) as model to forecast the electrical load using various hidden layers consist of one, two, and three layers. In this research, secondary data pre�processing is going to be done with add empty data, remove double data, and remove time with random interval. The electrical load data for 5 years is divided into 2 types of datasets, namely training data and test data. The raining data using data consist of 4 years electrical load from 2013-2016, while the test data uses data in 2017. LSTM Model then compared with Random Forest (RF) and Support Vector Machine (SVM). From the experimental result, the Root Mean Square Error (RSME) for LSTM model with 2 has the lowest compare to SVM and RF.

Item Type:	Other
Subjects:	#3 Repository of Lecturer Academic Credit Systems (TPAK) > Corresponding Author
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	Mr. Bhakti Suprapto
Date Deposited:	30 Apr 2023 06:03
Last Modified:	30 Apr 2023 06:03
URI:	http://repository.unsri.ac.id/id/eprint/98403

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