Statistical Approaches to Forecasting Domestic Energy Consumption and Assessing Determinants: The Case of Nordic Countries

Ardakani, Samad Ranjbar; Hossein, Seyed Mohsen; Aslani, Alireza

doi:10.1080/10485236.2018.12016689

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…Factors such as population increase, energy production, infrastructural advancement as well as financial sector growth can have a significant impact on the economic growth or development of a country. Population growth, energy production and usage, and trade openness, according to the following studies and other researches such as Ardakani et al (2018) Aslani et al (2019), Rahman and Majumder, (2022), all result in increased emissions of greenhouse gases like CO 2 . All of these factors have contributed to economic prosperity, but they have also had a severe impact on the environment.…”

Section: Introductionmentioning

confidence: 85%

A Quantile Regression Analysis of the Impact of Electricity Production Sources on CO2 Emission in South Asian Countries

Majumder

Voumik

Rahman³

et al. 2023

SPEE

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This study wants to fill a gap in the empirical literature by looking at how the sources of electricity affect CO2 emissions in South Asian countries. Because of the consistent production levels and economic growth in South Asian countries, energy generation is becoming a key issue. The data, which covers from 1972 to 2015, is subjected to quantile regression (QR). The quantile regression coefficients’ findings are statistically significant at a 1% level of significance. According to the regression results, all energy generation sources and associated variables have a positive impact on CO2 emissions. Coal-fired power plants have a bigger impact on the environment than other types of pollution. On the other hand, renewable energy sources have the minimum impact on environmental degradation. Possible alternatives for reducing carbon dioxide emissions instead of coal, oil, and gas-based energy production sources have been presented. The policy implications also suggest that environmental policy should be improved by applying renewable energy, wind energy, hydroelectric sources, and nuclear energy. The link between economic growth and energy concentration needs to be looked into more with the help of more economic indicators and the parameters of electricity-generating sources.

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Section: Introductionmentioning

confidence: 85%

A Quantile Regression Analysis of the Impact of Electricity Production Sources on CO2 Emission in South Asian Countries

Majumder

Voumik

Rahman³

et al. 2023

SPEE

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“…As reported by Biswas et al [20], several researchers have supported that ANNs are more effective than linear regression methods for modeling energy consumption; nevertheless, regression models remain simpler and more understandable [21]. Furthermore, ANNs have been found to be more accurate than linear regression, SVR [22], k-nearest neighborhood (KNN) and multivariate adaptive regression splines (MARS) methodologies [23] for modeling nonlinear fluctuating phenomena. ANNs can model energy consumption based on hourly intervals [24] or even sub-hourly intervals (e.g., 15 minutes) [25].…”

Section: Machine Learning Algorithmsmentioning

confidence: 92%

Energy use and CO2 emissions of the Moroccan transport sector

Oubnaki,

Haouraji,

Mounir

et al. 2024

IJECE

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In this paper, optimized models based on two different machine learning (ML) methods were developed to forecast the transport energy consumption (TEC) and carbon dioxide (CO2) emissions in Morocco by 2030. More precisely, artificial neural networks (ANN) and support vector regression (SVR) were used for modelling non-linear TEC and CO2 emissions data. This study uses data from 1990 to 2020 and employs various independent parameters, including population, gross domestic product, urbanization rate, evolution of the number of vehicles, and the number of electric vehicle introductions. Four statistical metrics are derived to assess the effectiveness of the ML algorithms used. The forecasts for 2030 were based on six scenarios, including three scenarios for the growth of gross domestic product (GDP) and two scenarios for the evolution of electric cars’ introduction into Moroccan vehicle fleet. The ANN model outputs showed that a decrease in TEC and CO2 emissions is expected until 2030. However, the SVR model predicts outputs values close to those in 2020. The study's results also indicate that: i) TEC and transport CO2 emissions are positively impacted by economic growth in Morocco and ii) electric vehicles will be essential components enabling substantial reductions in overall CO2 emissions in future transport systems.

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“…Although ANN has a higher level of accuracy, MLR models are simple and understandable for non-professionals [61]. Moreover, ANN has been found to be more accurate than Linear regression, support vector machine [62] and, MARS [63] methodologies for modeling nonlinear fluctuating phenomena [64]. ANN is capable of modeling energy consumption based on hourly intervals [65] or even less than hourly intervals (e.g., 15 min) [66].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

et al. 2020

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Energy has a strategic role in the economic and social development of countries. In the last few decades, energy demand has been increasing exponentially across the world, and predicting energy demand has become one of the main concerns in many countries. The residential and commercial sectors constitute about 34.7% of global energy consumption. Anticipating energy demand in these sectors will help governments to supply energy sources and to develop their sustainable energy plans such as using renewable and non-renewable energy potentials for the development of a secure and environmentally friendly energy system. Modeling energy consumption in the residential and commercial sectors enables identification of the influential economic, social, and technological factors, resulting in a secure level of energy supply. In this paper, we forecast residential and commercial energy demands in Iran using three different machine learning methods, including multiple linear regression, logarithmic multiple linear regression methods, and nonlinear autoregressive with exogenous input artificial neural networks. These models are developed based on several factors, including the share of renewable energy sources in final energy consumption, gross domestic production, population, natural gas price, and the electricity price. According to the results of the three machine learning methods applied in our study, by 2040, Iranian residential and commercial energy consumption will be 76.97, 96.42 and 128.09 Mtoe, respectively. Results show that Iran must develop and implement new policies to increase the share of renewable energy supply in final energy consumption.

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Statistical Approaches to Forecasting Domestic Energy Consumption and Assessing Determinants: The Case of Nordic Countries

Cited by 10 publications

References 44 publications

A Quantile Regression Analysis of the Impact of Electricity Production Sources on CO2 Emission in South Asian Countries

A Quantile Regression Analysis of the Impact of Electricity Production Sources on CO2 Emission in South Asian Countries

Energy use and CO<sub>2</sub> emissions of the Moroccan transport sector

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

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