Atif Maqbool Khan

Dr. Atif Maqbool Khan

Faculty of Energy and Fuels, AGH University of Science and Technology, Kraków, Małopolskie, Poland
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Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 August 2017 in Renewable and Sustainable Energy Reviews
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Journal Article
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MAMumtaz Ahmed
Mumtaz Ahmed
Atif Maqbool KhanAtif Maqbool Khan
Atif Maqbool Khan
SBSalma Bibi
Salma Bibi
MZMuhammad Zakaria
Muhammad Zakaria
1 February 2023 in Expert Systems with Applications
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Journal Article
Open access
Atif Maqbool KhanAtif Maqbool Khan
Atif Maqbool Khan
MOMagdalena Osińska
Magdalena Osińska
The study compares the forecasting performance of the grey type models, represented by an optimized nonlinear grey Bernoulli model (ONGBM), a nonlinear grey Bernoulli model with particle swarm optimization (NGBM-PSO), and a standard GM with a classic time series model, represented by Auto-Regressive Integrated Moving Average (ARIMA). The models are compared based on simulations and energy consumption forecasting in Brazil and India at aggregate and disaggregate levels from 1992 to 2019. The study illustrates the picture of the Brazil and India energy consumption nexus at aggregate and disaggregated levels. The forecasting accuracy is compared using standard measures such as MAPE, MSE, RMSE, and normalized RMSE. The Diebold-Mariano test findings validates the ARIMA (1,1,1), GM (1,1), ONGBM (1,1) and NGBM (1,1)-PSO models' equal predictive performance. The models are used to compute forecast combinations, ensuring smaller forecasting errors than single models. Optimizing NGBM (1,1) using two algorithms ensures the highest forecasting efficiency for short time series. The results allow a recommendation to use ONGBM (1,1) and NGBM (1,1)-PSO models in the short-term forecasting of energy consumption and combine these forecasts with forecasts from GM (1,1) and ARIMA (1,1,1) models in practical applications.
1 December 2015 in Renewable and Sustainable Energy Reviews
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MAMumtaz Ahmed
Mumtaz Ahmed
KRKhalid Riaz
Khalid Riaz
Atif Maqbool KhanAtif Maqbool Khan
Atif Maqbool Khan
SBSalma Bibi
Salma Bibi
11 May 2021 in Energies
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Atif Maqbool KhanAtif Maqbool Khan
Atif Maqbool Khan
MOMagdalena Osińska
Magdalena Osińska
Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to predict energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the predictive ability of the FGM1,1 with traditional ones, like standard GM1,1 and ARIMA1,1,1 models. Moreover, it illustrates the view of BRICS’s nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM1,1 for a specific range of order parameters and the ARIMA1,1,1 model and the usefulness of both approaches for energy consumption efficient forecasting.
22 October 2022 in Energies
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Atif Maqbool KhanAtif Maqbool Khan
Atif Maqbool Khan
MOMagdalena Osińska
Magdalena Osińska
Due to industrialization, urbanization, and rapid population increases, the worldwide energy demand is increasing daily. The need for energy meets limitations, and searching for new energy sources is crucial, particularly for European countries. Energy crises occur temporarily due to different circumstances and cause oil price fluctuations. The present study aims to identify circular economy (CE) determinants that conditionally increase/decrease energy use to remain sustainable. It focuses on the linkage between energy consumption and circular economy in the European Union (EU) member states. In the econometric panel model specifications, two alternative endogenous variables are considered, i.e., total energy consumption and energy consumption from renewable sources. The results demonstrate that the selected CE indicators decreased the former and increased the latter. The data covered the period from 2010–2019. The significance of this study relies upon identifying the current level of CE implementation in the EU countries in the context of reducing total energy consumption and increasing the share of energy from renewable sources.
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27 July 2024 in Energies
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MOMagdalena Osińska
Magdalena Osińska
Atif Maqbool KhanAtif Maqbool Khan
Atif Maqbool Khan
JKJacek Kwiatkowski
Jacek Kwiatkowski
This study aims to identify the factors most likely to affect renewable energy consumption (REC) across mostly homogenous country groups worldwide. Classifying countries into a relatively homogenous group is taken from their economic and social development level measured with the Human Development Index. We delimited highly, medium-, and low-developed countries and checked whether the sets of determinants for using renewable energy sources are the same. We constructed a panel dataset as a basis for the panel Bayesian model averaging (panel BMA) as a factor selection method. The most likely factors were found and compared between the groups of countries. Then, the panel fixed-effects models for each country group were estimated. The results allowed us to confirm that CO2 per capita emissions, terms of trade, GDP, foreign direct investment, crude oil price, and energy consumption from alternative sources are the most critical drivers of REC in group I. The most important factors in group II are CO2 per capita, labor force, forest area, and gas and coal consumption. In the third group, REC consumption differs from that of the more advanced groups and strongly depends on foreign direct investment inflow. The results allow the formulation of policy recommendations on a global scale.