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Machine learning for a sustainable energy future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …
demands advances—at the materials, devices and systems levels—for the efficient …
Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the …
The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …
autonomous software that optimizes decision-making and energy distribution operations …
Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis
Wind energy increasingly attracts investment from many countries as a clean and renewable
energy source. Since wind energy investment cost is high, the efficiency of a potential wind …
energy source. Since wind energy investment cost is high, the efficiency of a potential wind …
[HTML][HTML] Energetics Systems and artificial intelligence: Applications of industry 4.0
Industrial development with the growth, strengthening, stability, technical advancement,
reliability, selection, and dynamic response of the power system is essential. Governments …
reliability, selection, and dynamic response of the power system is essential. Governments …
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
The energy industry is at a crossroads. Digital technological developments have the
potential to change our energy supply, trade, and consumption dramatically. The new …
potential to change our energy supply, trade, and consumption dramatically. The new …
The pathway to curb greenwashing in sustainable growth: The role of artificial intelligence
D Zhang - Energy Economics, 2024 - Elsevier
Artificial Intelligence (AI) can improve production efficiency and general quality of life
through assisting human labor, potentially leading to the conversion of employment types …
through assisting human labor, potentially leading to the conversion of employment types …
[HTML][HTML] Methods of forecasting electric energy consumption: A literature review
RV Klyuev, ID Morgoev, AD Morgoeva, OA Gavrina… - Energies, 2022 - mdpi.com
Balancing the production and consumption of electricity is an urgent task. Its implementation
largely depends on the means and methods of planning electricity production. Forecasting is …
largely depends on the means and methods of planning electricity production. Forecasting is …
Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
Artificial intelligence and machine learning for energy consumption and production in emerging markets: a review
D Mhlanga - Energies, 2023 - mdpi.com
An increase in consumption and inefficiency, fluctuating trends in demand and supply, and a
lack of critical analytics for successful management are just some of the problems that the …
lack of critical analytics for successful management are just some of the problems that the …
A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions
China, India, and the USA are the world's biggest energy consumers and CO 2 emitters.
Being the leading contributors to climate change, these economies are also at the core of …
Being the leading contributors to climate change, these economies are also at the core of …