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Earth5R Research Article: Assessing The Impact Of Artificial Intelligence On Renewable Energy In India

Author Name: Ankur Kumar

Earth5R Sustainability ID: E5R695R5B55U950

School/Institute/ Affiliation: National Institute of Technology, Srinagar, India.

Publishing Platform: Earth5R Earth Journal (earth5r.org)

Blog Content
Artificial intelligence (AI) is a transformative technology with the potential to revolutionize the renewable energy sector in India. AI can help India achieve its ambitious renewable energy goals by maximizing energy production and minimizing costs. Within the field of computer science, artificial intelligence (AI) comprises many methods and algorithms that enable energy systems to analyze intricate data patterns, identify patterns, and generate predictions based on data. AI may be used to carefully assess energy usage patterns, which makes it possible to find areas for optimization and improvement. Moreover, machine learning (ML), a branch of artificial intelligence, enables energy systems to learn from past data, adjust to changing conditions, and make decisions on their own based on experience. Large datasets can be automatically analyzed by ML algorithms to find patterns, correlations, and anomalies. This process makes it possible to find insights that would be difficult or time- consuming for humans to find.

With a projected growth rate of 39.4%, the worldwide AI market is anticipated to reach US$ 422.37 billion by 2028. With a compound annual growth rate (CAGR) of 20.2%, the AI market in India is expected to reach US$ 7.8 billion by 2025. AI adoption can be very beneficial to the renewable energy business, as it is fast changing many facets of our lives. Artificial Intelligence (AI) presents a plethora of intriguing applications in optimizing renewable energy systems. The renewable energy industry may reduce operating costs, maximize maintenance schedules, anticipate energy production more accurately, and increase efficiency by utilizing AI.

Key applications of AI in the renewable energy sector include:

  • Forecasting renewable energy generation: AI can predict solar and wind power generation with greater accuracy, helping grid operators manage the grid more effectively.
  • Optimizing renewable energy systems: AI can optimize solar panels and wind turbines to improve their efficiency and performance.
  • Predicting and preventing maintenance issues: AI can predict and prevent maintenance issues with renewable energy systems, reducing downtime and costs.
  • Improving grid management: AI can help grid operators integrate renewable energy sources into the grid more effectively and balance supply and demand in real-time.
  • Predicting Consumer Demand: AI provides utilities with the ability to make precise predictions of consumer electricity demand. By harnessing AI’s forecasting capabilities, utilities can take proactive measures to efficiently oversee the grid, ensuring that there is ample power generation to satisfy the expected demand. This proactive approach helps in averting potential blackouts or fluctuations in power supply, ultimately bolstering the reliability of the energy grid.

AI offers several critical benefits for the renewable vitality division in India.

  1. Expanded effectiveness: AI can offer assistance to progress the effectiveness of renewable vitality frameworks by optimizing their plan and operations. For case, AI can be utilized to create more effective sun-oriented boards and wind turbines and to optimize the situation of renewable vitality systems.
  2. Diminished costs: AI can offer assistance to diminish the costs of renewable vitality by making it more productive and by creating modern advances that are more reasonable. For illustration, AI can be utilized to create unused strategies for fabricating renewable vitality components and to create other ways to store and disseminate renewable energy.
  3. Moved forward unwavering quality: AI can offer assistance to make strides in the unwavering quality of renewable vitality frameworks by recognizing and anticipating potential issues. For illustration, AI can be utilized to screen the execution of renewable vitality frameworks and to anticipate when support is needed.
  4. Upgraded supportability: AI can offer assistance to improve the maintainability of the renewable vitality division by making a difference in decreasing squandering and contamination.
    For this case, AI can be utilized to create more effective ways to reuse renewable energy components and to create other ways to diminish the natural effect of renewable vitality frameworks.

Here are some machine learning algorithms that can be used for renewable energy production
and management:

  1. Linear regression: Linear regression is a simple but powerful algorithm that can be used to model the relationship between two variables. It is often used to predict renewable energy production, such as solar and wind power.
  2. Logistic regression: Logistic regression is a similar algorithm to linear regression, but it is used to model the probability of a binary outcome, such as whether or not a solar panel will generate electricity.
  3. Support vector machines (SVMs): SVMs are a type of machine learning algorithm that can be used for classification and regression tasks. They are often used to predict renewable energy production and to classify renewable energy resources.
  4. Random forests: Random forests are an ensemble learning algorithm that combines the predictions of many individual decision trees. They are often used to predict renewable energy production and to classify renewable energy resources.
  5. Gradient boosting machines: Gradient boosting machines are another ensemble learning algorithm that combines the predictions of many individual decision trees. They are often used to predict renewable energy production and to classify renewable energy resources.
  6. Neural networks: Neural networks are a type of machine learning algorithm that is inspired by the human brain. They are often used to predict renewable energy production and to identify patterns in renewable energy data.
  7. Deep learning: Deep learning is a subfield of machine learning that uses neural networks to learn complex patterns in data. Deep learning algorithms are often used to predict renewable energy production and to identify patterns in renewable energy data.
  8. Time series analysis: Time series analysis is a set of techniques that can be used to analyze and forecast time series data. Time series analysis is often used to predict renewable energy production and to identify patterns in renewable energy data.
  9. Clustering: Clustering is a machine learning algorithm that can be used to group similar data points. Clustering is often used to identify different types of renewable energy resources and to group renewable energy resources based on their characteristics.
  10. Anomaly detection: Anomaly detection is a machine learning algorithm that can be used to identify unusual or unexpected data points. Anomaly detection is often used to identify problems with renewable energy systems and to detect fraud in renewable energy markets.
  11. Predictive maintenance: Predictive maintenance is a machine learning technique that can be used to predict when a machine or system is likely to fail. Predictive maintenance can be used to predict when renewable energy systems need to be serviced or repaired, which can help to reduce costs and improve reliability.
  12. Demand forecasting: Demand forecasting is a machine learning technique that can be used to predict the demand for renewable energy. Demand forecasting can help to ensure that there is enough renewable energy available to meet demand and avoid overproduction.

Results
Here are some specific examples of AI being used in India’s renewable energy sector:
(1) Tata Power:
Tata Power uses AI to predict solar energy production from its factory’s solar power plant. This helps the company better manage its grid and ensure it has enough power to meet demand.

(2) ReNew Power:
ReNew Power uses artificial intelligence to increase the efficiency of wind turbines. This helps the company produce more electricity from wind farms.

(3) O&M Power: O&M Power uses artificial intelligence to predict and prevent maintenance problems of solar power plants. This helps companies reduce time and costs.

(4) Power Grid Corporation of India (PGCIL):
PGCIL is using artificial intelligence to improve the management of the power grid. This helps the company better integrate renewable energy into the grid and instantly balance supply and demand.

Worldwide these companies are using AI in a variety of ways to improve the efficiency,
performance, and reliability of renewable energy production and management systems.
For example:

  • Google AI is developing AI algorithms to forecast wind energy generation and solar energy generation.
  • Vestas is using AI to develop predictive maintenance solutions for wind turbines.
  • Siemens Gamesa Renewable Energy is using AI to develop wind turbines that are more efficient and have a longer lifespan.
  • O&M Power is using AI to predict and prevent maintenance issues with its solar power plants.
  • PGCIL is using AI to improve the management of the electricity grid in India.
  • MIT researchers are using AI to develop new solar cell materials that are more efficient and less expensive to produce.
  • Tesla is using AI to develop solar energy storage systems that can be used to store solar energy generated during the day and used at night or when the sun is not shining.
  • SolarEdge is using AI to develop predictive maintenance solutions for solar power plants.
  • DeepMind is using AI to develop algorithms to optimize the performance of renewable energy systems.
  • Autogrid is using AI to develop software to help grid operators manage the grid with a high share of renewable energy.

Conclusion
In conclusion, AI has the potential to play a significant role in helping India achieve its renewable energy goals. Smart technologies can help make renewable energy more affordable and accessible by increasing the efficiency and effectiveness of renewable energy systems. AI can also help improve grid management, making it easier to connect to renewable energy.

References

  1. (63) Artificial Intelligence (AI) in Renewable Energy Systems: A Condensed Review of its Applications
    and Techniques | Jeffrey Dellosa – Academia.edu :
    https://www.academia.edu/62001240/Artificial_Intelligence_AI_in_Renewable_Energy_Systems_A_Condensed_Review_
  2. (63) Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey | Sheikh Yawar – Academia.edu : https://www.academia.edu/83170251/Machine_Learning_Techniques_for_Supporting_Renewable_Energy_Generation_f4eb-4afa-9f4f-dac5926045d6&rw_pos=0
  3. (63) Artificial Intelligence intervention to Urban Building Renewable Energy Modeling Intervention for Robust Flexible Communities | Sammar Allam – Academia.edu : https://www.academia.edu/76160536/Artificial_Intelligenceintervention_to_Urban_Building_Renewable_Energy