AI in Sustainable Energy: Applications and Innovations

Abstract

Incorporation of Artificial Intelligence (AI) into sustainable energy systems is a major achievement in maximizing efficient energy generation, distribution, and usage. This study examines the potential of AI to be a transformative technology, complements existing studies and attempts to pinpoint potential gaps that require further research while introducing a new model for adoption. The evaluation identifies specific AI and machine learning techniques that are technically viable, as well provides a multicriteria a,pproach for ranking energy applications to be opportunities for AI and machine learning. Leading applications encompass solar and wind prediction, fault diagnostics, and grid stability, among others, although associated issues related to for instance intermittency and computational scalability continue as well. The moral and environmental implications of using AI as it relates to more than just optimizing the technology itself are discussed. The idea of ‘Sustainable AI’ is proposed, in an effort to promote a conceptual frame that integrates environmental sustainability with considerations of social equity. A combination of LDA, BERT, and clustering on topic modeling is utilized to break down the literature into the eight main research themes, such as smart buildings, and renewable energy evaluation. The findings give rise to 14 recommendations for future research, which can provide a guide for policymakers and practitioners to synthesize theoretical knowledge with field practice. The use and potential of A.I . brings also new emerging risks as well as opportunities for progress in over 134 Sustainable Development Goal (SDG) targets . Challenges, such as the ‘black box’ problem of machine learning, privacy concerns, and the energy requirements of machine learning algorithms, require governance and to be puzzles over . It concludes by highlighting the need for interdisciplinarity, suggesting a form of STEAM ( STEM + Arts) approach to help advance innovation in an inclusive manner . Policy-based recommendations highlight support for mixed renewable systems, adaptable ML designs, and life-cycle- conscious AI research. The review thus also ultimately lays out a pathway for using AI to set in motion socio-technical carbon-neutral energy transitions.

Introduction

The transition to sustainable energy is urgent due to rising energy demand, climate change, and resource depletion. Renewable sources like solar and wind are essential but present challenges due to their variability. Artificial Intelligence (AI) offers solutions through improved forecasting, optimization, predictive maintenance, and energy efficiency, but it also brings issues such as high data and computational needs, ethical concerns, and environmental impacts.

The paper systematically reviews literature (2021–2025) on AI in sustainable energy, using advanced topic modeling (BERT, LDA) and multi-criteria analysis to identify trends and gaps. It evaluates 182 peer-reviewed articles, highlighting AI's role across multiple sectors: smart grids, renewable energy forecasting, energy storage, smart cities, agriculture, and climate science.

Key findings:

• AI greatly improves energy forecasting, grid reliability, and resource optimization.

• Popular AI methods include Neural Networks (ANN, CNN, LSTM), Support Vector Machines (SVM), and hybrid models like ANN + GA or Kalman filters.

• AI can contribute positively to Sustainable Development Goals (SDGs), but also risks reinforcing inequality and environmental degradation due to energy-intensive model training.

• Ethical AI governance and interdisciplinary collaboration are critical.

• Sustainable AI includes both using AI for sustainability goals and making AI itself more environmentally friendly.

A case-based ranking using Analytic Hierarchy Process (AHP) found that Alternative 4 (A4)—a balanced approach considering technical, economic, environmental, social, and policy aspects—was the most effective.

Emerging themes:

1. Sustainable buildings and energy consumption

2. AI for water management and agriculture

3. Smart cities and IoT integration

4. Renewable energy optimization

5. AI ethics, education (STEAM), and governance

Challenges include:

• Data quality and model generalization

• Lack of integration across energy sectors

• High energy use of AI models

• Policy gaps and ethical concerns

The study calls for:

• Interdisciplinary research

• Ethical AI development

• Customized AI systems based on local needs

• Better education and transparency in AI

Conclusion

AI is really important for moving to sustainable energy. It can really change how we predict, improve, and control renewable energy systems in real-time. This look at things uses a way to see how much better using data is than older ways, but it says we need to think about tech, money, the environment, society, and the rules about all this. We\'re starting to see new research on making the most of energy that\'s spread out, using AI with new tech like 5G, blockchain, and edge computing, and making transportation more sustainable. Putting AI and good software practices together is key to making our energy stuff work better, be stronger, and adapt to changes, which helps us get to a carbon-neutral economy. Even though AI can do a lot for Sustainable Development Goals and smarter, greener stuff, there are still some big problems. Training AI uses a lot of energy, it\'s not always clear how it works, there are ethical worries, and we need better data systems. To fix these, we need AI that is easier to understand, fair, and doesn\'t use much energy, and we need good rules and people from different fields to work together. To teach people the skills they need for sustainable energy, we need to include science, tech, engineering, arts, and math in a complete way. Also, Sustainable AI should be more than just AI for Good. It needs to think about the environment, fair use of resources, and social justice when making AI. For the future, it\'s important for those who make rules, researchers, businesses, and teachers to work together. We need to push forward with combined and physics-based machine learning, grow AI-run energy grids that are spread out, make energy storage better, and add electric cars to smart energy systems. In the end, this review shows how to use AI well and in a way that we\'re responsible for—getting all the good things about renewable energy while dealing with the tricky social, tech, and ethical parts of changing how we get energy.

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Copyright

Copyright © 2025 Er. Sandesh Lamichhane, Dr. Ningthoujam Chidananda Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.