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Electricity Consumption Forecasting in Algeria using ARIMA and Long Short-Term Memory Neural Network

Domaine:

environment and energy

Type de record:

paper
Créateur:
AbdHac
Éditeur:
Uni
Éditeur:
CCSDuni
Hôte:avatar
International audience Forecasting electricity consumption is necessary for electric grid operation and utility resource planning, as well as to improve energy security and grid resilience. Thus, this research aims to investigate the prediction performance of the ARIMA and LSTM neural network model using electricity consumption data during the period 1990 to 2020. The time series for electricity consumption is divided into 70% for training data and 30% for test data. The results showed that the LSTM model provided improved forecasting accuracy than the ARIMA model.

Visit

cnrs.hal.science

Tags

Algeria. JEL Classification Codes: Q47LSTMARIMAElectricity ConsumptionC45C53Electricity Consumption ARIMA LSTM Algeria. JEL Classification Codes: Q47[QFIN]Quantitative Finance [q-fin]

Licenses

http://creativecommons.org/licenses/by/info:eu-repo/semantics/OpenAccess

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