Comparative study of forecasting methods for energy demand in Morocco

Nafil, Abdellah; Bouzi, Mostafa; Anoune, Kamal; Ettalabi, Naoufl

doi:10.1016/j.egyr.2020.09.030

Cited by 23 publications

(7 citation statements)

References 18 publications

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“…While the LR model only simulates a simple linear correlation between the input and output variables [19,20], the ARIMA model mainly considers the auto-correlation of the time series. The linear models are quite popular in the application of energy forecasting and have been used to forecast oil consumption [19], electricity consumption [21,22], demand [20,23], wind generation [24], total energy demand and supply [25], etc. However, the ARIMA model often suffers from "overdifferece" [26], and both of these linear models are limited in describing nonlinear data sets.…”

Section: The Structured Models For Energy Forecastingmentioning

confidence: 99%

Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States

Cai

Yuan

et al. 2023

Sustainability

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Energy forecasting based on univariate time series has long been a challenge in energy engineering and has become one of the most popular tasks in data analytics. In order to take advantage of the characteristics of observed data, a partially linear model is proposed based on principal component analysis and support vector machine methods. The principal linear components of the input with lower dimensions are used as the linear part, while the nonlinear part is expressed by the kernel function. The primal-dual method is used to construct the convex optimization problem for the proposed model, and the sequential minimization optimization algorithm is used to train the model with global convergence. The univariate forecasting scheme is designed to forecast the primary energy consumption of the electric power sector of the United States using real-world data sets ranging from January 1973 to January 2020, and the model is compared with eight commonly used machine learning models as well as the linear auto-regressive model. Comprehensive comparisons with multiple evaluation criteria (including 19 metrics) show that the proposed model outperforms all other models in all scenarios of mid-/long-term forecasting, indicating its high potential in primary energy consumption forecasting.

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Section: The Structured Models For Energy Forecastingmentioning

confidence: 99%

Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States

Cai

Yuan

et al. 2023

Sustainability

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show abstract

“…Nafil et al. [ 32 ] compared three forecasting methods (ARIMA, temporal causality modeling, and exponential smoothing) to calculate Morocco's 2020 energy demand forecast. Ye et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach

2022

Energy

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“…The data used to establish their different models was collected on a smart grid system of a residence based in Xindian, Taiwan. Nafil et al [14] in their work on the prediction of Moroccan energy consumption, established three prediction models namely an exponential smoothing model, a temporal causality model and an ARIMA model. They used data from 1981 to 2016 to develop and validate their models.…”

Section: Literature Reviewmentioning

confidence: 99%

Linear Regression and ARIMA Models for Electricity Demand Forecasting in West Africa

Semekonawo

Kam

2022

JENRR

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This article focuses on the predictive study of electricity demand in West African countries based on the multivariate linear regression model and the ARIMA model. The objective of the study is first to establish for each country a linear regression model and ARIMA model, then to compare the two (2) models based on the MAD, RMSE and MAPE coefficients, and finally to deduce of this comparison the best valid model to establish the electricity demand prediction of the country. We have come to the conclusion that the ARIMA model is more adequate for predicting the electricity demand of most of West African countries with the exception of Gambia, Ghana, Guinea, Liberia and Nigeria where the multivariate linear regression model performs better.

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Comparative study of forecasting methods for energy demand in Morocco

Cited by 23 publications

References 18 publications

Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States

Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States

Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach

Linear Regression and ARIMA Models for Electricity Demand Forecasting in West Africa

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