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As the human population continues to grow and use resources, we must be mindful of our planet’s natural limits. Sustainable development provides a pathway to maintain and improve human life now while also ensuring that future generations will have the resources that they need. The long-term success of sustainability efforts rests on understanding the interplay between human actions and ecological systems.
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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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Machine learning for a sustainable energy future.

Zhenpeng Yao1,2,3,4, Yanwei Lum5,6, Andrew Johnston6

  • 1Shanghai Key Laboratory of Hydrogen Science & Center of Hydrogen Science, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.

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Machine learning (ML) accelerates renewable energy research by improving energy harvesting, storage, conversion, and management. This perspective outlines ML

Keywords:
BatteriesComputer scienceElectrocatalysisEnergy grids and networksSolar cells

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Area of Science:

  • Energy Science and Engineering
  • Materials Science
  • Computer Science

Background:

  • Global transition to renewable energy requires advancements in energy harvesting, storage, conversion, and management.
  • Machine learning (ML) is increasingly adopted by energy researchers to expedite these advancements.

Purpose of the Study:

  • To highlight recent progress in ML-driven energy research.
  • To outline current and future challenges in applying ML to energy systems.
  • To define requirements for optimal utilization of ML techniques in energy research.

Main Methods:

  • Review and evaluation of recent ML applications in photovoltaics, batteries, electrocatalysis, and smart grids.
  • Introduction of key performance indicators for assessing ML-accelerated energy research workflows.
  • Discussion of potential future research directions for ML in the energy sector.

Main Results:

  • ML techniques are demonstrating significant potential across various renewable energy domains.
  • Key performance indicators are proposed to quantitatively compare ML-driven research approaches.
  • Specific areas benefiting from ML in energy harvesting, storage, conversion, and management are identified.

Conclusions:

  • ML is a transformative tool for accelerating the development of renewable energy technologies.
  • Addressing current challenges and establishing clear metrics are crucial for maximizing ML's impact.
  • Further research integrating ML holds promise for overcoming critical energy challenges.