Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey
- URL: http://arxiv.org/abs/2406.16965v2
- Date: Sat, 19 Oct 2024 19:23:48 GMT
- Title: Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey
- Authors: Abdur Rashid, Parag Biswas, Angona Biswas, MD Abdullah Al Nasim, Kishor Datta Gupta, Roy George,
- Abstract summary: Artificial intelligence (AI) has become a crucial instrument for streamlining processes in various industries.
Nine AI-based strategies are identified here to assist Renewable Energy (RE) in contemporary power systems.
This study also addressed three main topics: using AI technology for renewable power generation, utilizing AI for renewable energy forecasting, and optimizing energy systems.
- Score: 0.0
- License:
- Abstract: Artificial intelligence (AI) has become a crucial instrument for streamlining processes in various industries, including electrical power systems, as a result of recent digitalization. Algorithms for artificial intelligence are data-driven models that are based on statistical learning theory and are used as a tool to take use of the data that the power system and its users generate. Initially, we perform a thorough literature analysis of artificial intelligence (AI) applications related to renewable energy (RE). Next, we present a thorough analysis of renewable energy factories and assess their suitability, along with a list of the most widely used and appropriate AI algorithms. Nine AI-based strategies are identified here to assist Renewable Energy (RE) in contemporary power systems. This survey paper comprises an extensive review of the several AI techniques used for renewable energy as well as a methodical analysis of the literature for the study of various intelligent system application domains across different disciplines of renewable energy. This literature review identifies the performance and outcomes of nine different research methods by assessing them, and it aims to distill valuable insights into their strengths and limitations. This study also addressed three main topics: using AI technology for renewable power generation, utilizing AI for renewable energy forecasting, and optimizing energy systems. Additionally, it explored AI's superiority over conventional models in controllability, data handling, cyberattack prevention, smart grid implementation, robotics- AI's significance in shaping the future of the energy industry. Furthermore, this article outlines future directions in the integration of AI for renewable energy.
Related papers
- AI-Driven Approaches for Optimizing Power Consumption: A Comprehensive Survey [0.0]
Reduced environmental effect, lower operating costs, and a stable and sustainable energy supply are the main reasons why power optimization is important.
Power optimization and artificial intelligence (AI) integration are essential to changing the way energy is produced, used, and distributed.
Real-time monitoring and analysis of power usage trends is made possible by AI-driven algorithms and predictive analytics.
arXiv Detail & Related papers (2024-06-22T04:42:37Z) - Green Edge AI: A Contemporary Survey [46.11332733210337]
The transformative power of AI is derived from the utilization of deep neural networks (DNNs)
Deep learning (DL) is increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs)
Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL.
arXiv Detail & Related papers (2023-12-01T04:04:37Z) - Exploring Artificial Intelligence Methods for Energy Prediction in
Healthcare Facilities: An In-Depth Extended Systematic Review [0.9208007322096533]
This study conducted a literature review, utilizing the PRISMA framework, of articles that employed machine learning and artificial intelligence techniques for predicting energy consumption in hospital buildings.
This review revealed a diverse range of data inputs influencing energy prediction, with occupancy and meteorological data emerging as significant predictors.
The findings underscore the immense potential of AI in optimizing hospital energy consumption but also highlight the need for more comprehensive and granular research.
arXiv Detail & Related papers (2023-11-27T13:30:20Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs.
This paper explores the innovative concept of harnessing these AI-generated images as new data sources.
In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
arXiv Detail & Related papers (2023-10-03T06:55:19Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - AI Explainability and Governance in Smart Energy Systems: A Review [0.36832029288386137]
Lack of explainability and governability of AI is a major concern for stakeholders.
This paper provides a review of AI explainability and governance in smart energy systems.
arXiv Detail & Related papers (2022-10-24T05:09:13Z) - Machine learning applications for electricity market agent-based models:
A systematic literature review [68.8204255655161]
Agent-based simulations are used to better understand the dynamics of the electricity market.
Agent-based models provide the opportunity to integrate machine learning and artificial intelligence.
We review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models.
arXiv Detail & Related papers (2022-06-05T14:52:26Z) - Learning, Computing, and Trustworthiness in Intelligent IoT
Environments: Performance-Energy Tradeoffs [62.91362897985057]
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications.
This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
arXiv Detail & Related papers (2021-10-04T19:41:42Z) - Artificial Intelligence Based Prognostic Maintenance of Renewable Energy
Systems: A Review of Techniques, Challenges, and Future Research Directions [3.1123064748686287]
Data Analytics and Machine Learning (ML) techniques are being used to increase the overall efficiency of these prognostic maintenance systems.
This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature.
Being a key aspect of ML-based solutions, we also discuss some of the commonly used publicly available datasets in the domain.
arXiv Detail & Related papers (2021-04-20T11:41:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.