Forecasting the Total Non-coincidental Monthly System Peak Demand in the Philippines: A Comparison of Seasonal Autoregressive Integrated Moving Average Models and Artificial Neural Networks

Forecasting the Total Non-coincidental Monthly System Peak Demand in the Philippines: A Comparison of Seasonal Autoregressive Integrated Moving Average Models and Artificial Neural Networks

This paper aims to determine suitable seasonal autoregressive integrated moving average (SARIMA) and feed-forward neural network (FFNN) models to forecast the total non-coincidental monthly system peak demand in the Philippines. To satisfy the stationary requirement of the SARIMA model, seasonal di...

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Bibliographic Details
Main Author: Samuel John Parreno
Format: Article
Language:English
Published: EconJournals 2023-09-01
Series:International Journal of Energy Economics and Policy
Subjects:
Electricity Peak Demand
Time Series Analysis
SARIMA
Artificial neural networks
Online Access:http://econjournals.com/index.php/ijeep/article/view/14240

Internet

http://econjournals.com/index.php/ijeep/article/view/14240

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