Predictive intelligence of machine learning models for global energy perspectives and transformations towards sustainability

Climate change is affecting ecosystems, communities, and human health worldwide. These changes pose risks to global energy systems so there is a dire need to combat climate change and limit global warming to 1.5°C. This study undertake global energy systems and forecasted total energy consumption, production and greenhouse gas (GHG) emissions worldwide for the study period 2021–2050 by taking the input data from 1970 to 2020 using the four algorithm’s namely, Holt Winter (HW), Exponential Smoothing (ES), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA) implemented in Python. It is found that HW and ES have same forecast results globally with energy consumption of 236,285 TWh which can easily meet by 475,980 TWh generation until 2050. Renewables and fossil fuels contributed to 250,106 TWh units and 225,874 TWh units with 48 billion metric tons of GHG emissions until 2050. The global forecast of ARIMA model suggested that 232,878 TWh energy consumption is noticed which can easily meet by 446,126 TWh generation with 213,052 TWh share of renewables and 233,074 TWh of fossil fuels with 49 billion metric tons of GHG emissions produced until 2050. SARIMA model forecast is very much valuable for limiting global mean temperature to 1.5 °C. The global energy consumption is forecasted to be 231,022 TWh which easily meet by 350,054 TWh green energy generation potential with almost zero emissions until 2050 and it is found that SARIMA model has 98% of accuracy. • Python based algorithms are used to achieve clean energy targets globally (1970–2050). • Forecasted global energy consumption, production, and GHG emissions using holt winter, exponential smoothing, ARIMA and SARIMA. • By 2040, renewable energy is projected to 100% in global power mix under SARIMA model. • Global CO 2 emissions must decrease by 60% in 2035 and 100% in 2040 under SARIMA model. • The transition to sustainable energy system is expected to boost global GDP by 15% until 2040.

Authors

Institutions

Publication Details

Journal
Energy Reports
Published
2026-03-11
DOI
https://doi.org/10.1016/j.egyr.2026.109202
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictive intelligence of machine learning models for global energy perspectives and transformations towards sustainability

Touqeer Ahmed Jumani, Ahmad Alshamayleh, Muhammad I. Masud, Mohammed Aman et al.
Energy Reports
Energy Load and Power Forecasting
article

Predictive intelligence of machine learning models for global energy perspectives and transformations towards sustainability

Touqeer Ahmed Jumani, Ahmad Alshamayleh, Muhammad I. Masud, Mohammed Aman, Abdul Karim, Muhammad Amir Raza, Muhammad Faheem
article en

Abstract

Climate change is affecting ecosystems, communities, and human health worldwide. These changes pose risks to global energy systems so there is a dire need to combat climate change and limit global warming to 1.5°C. This study undertake global energy systems and forecasted total energy consumption, production and greenhouse gas (GHG) emissions worldwide for the study period 2021–2050 by taking the input data from 1970 to 2020 using the four algorithm’s namely, Holt Winter (HW), Exponential Smoothing (ES), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA) implemented in Python. It is found that HW and ES have same forecast results globally with energy consumption of 236,285 TWh which can easily meet by 475,980 TWh generation until 2050. Renewables and fossil fuels contributed to 250,106 TWh units and 225,874 TWh units with 48 billion metric tons of GHG emissions until 2050. The global forecast of ARIMA model suggested that 232,878 TWh energy consumption is noticed which can easily meet by 446,126 TWh generation with 213,052 TWh share of renewables and 233,074 TWh of fossil fuels with 49 billion metric tons of GHG emissions produced until 2050. SARIMA model forecast is very much valuable for limiting global mean temperature to 1.5 °C. The global energy consumption is forecasted to be 231,022 TWh which easily meet by 350,054 TWh green energy generation potential with almost zero emissions until 2050 and it is found that SARIMA model has 98% of accuracy. • Python based algorithms are used to achieve clean energy targets globally (1970–2050). • Forecasted global energy consumption, production, and GHG emissions using holt winter, exponential smoothing, ARIMA and SARIMA. • By 2040, renewable energy is projected to 100% in global power mix under SARIMA model. • Global CO 2 emissions must decrease by 60% in 2035 and 100% in 2040 under SARIMA model. • The transition to sustainable energy system is expected to boost global GDP by 15% until 2040.

Energy ReportsVol. 15
Al-Ahliyya Amman University (JO), Mehran University of Engineering and Technology (PK), Indus University (PK), College of Applied Sciences, Nizwa (OM), University of Business and Technology (SA), A'Sharqiyah University, VTT Technical Research Centre of Finland (FI)
Openalex Percentile: Top 25%
Energy Load and Power Forecasting
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.