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The Impact of Artificial Intelligence and Machine Learning on Renewable Energy

POSTED 08/21/2024

Introduction

Artificial Intelligence (AI) and Machine Learning (ML) are proving as pioneers in a multitude of sectors and renewable energy is one of them. AI is a branch of computer science which deals with the development of intelligent systems that can, without any help, carry out tasks that would otherwise demand human intellect. Source: [MIT Technology Review](https://www.technologyreview.com))

In the opinion of Forbes, ML represents a part of AI that focuses on designing a set of instructions that enables a computer to learn from data to make certain predictions.

This type of energy derived from sunlight, wind, and water among others is crucial in fighting climate change and reduces the use of fossil energy. (Source:[Energy.gov](https://www.energy.gov).

AI and ML in Renewable Energy

I. Information management For energetic production

One of the most important uses of AI and ML in renewable energy is prediction. Based on tendencies that affect the weather, historical data on energy production, and live sensor data, the AI solution will be able to predict the energy generation rates with a high level of accuracy. (Source: [The Guardian](https://www.theguardian.com)).

However, as pointed out by Scientific American, this capability is essential for the sources like solar and wind, which are fluctuating and depend on the weather conditions by nature.

Examples of such applications include ability to forecast the output of photovoltaic panels from the level of cloud coverage and intensity of light for better management of the supply and demand by the grid operators.

II. The Efficiency of Energy Storage as well as its Distribution

MIT Technology Review also claims that energy storage is another industry where the application of AI and ML is quite significant.

Renewable energy is quite unpredictable and as a result there is a need to store the power to help meet the need as required.

AI algorithms can optimize the charging and discharging cycles of batteries, improving their lifespan and performance. (Source: [MIT Technology Review](https://www.technologyreview.com)).

Further, flexible grids can also be managed primarily through the help of AI that can then assign energy resources on the fly, thereby minimizing energy losses while making sure renewables are used optimally.

This optimization is critical in developing an enhanced smart grid as it prepares to interface with a soaring percentage of fossil energy.

III. Enhancing Energy Efficiency

AI and ML can also be used to achieve large advancements in the energy efficiency levels. AI integrated smart sensors and IoT appliances can watch and manage energy usage on a real-time basis, understand screw ups and propose adjustments for them. (Source: [Wired](https://www.wired.com)).

For instance, AI systems are capable of highly efficiently controlling the Heating Ventilation Air Conditioning (HVAC) systems in the buildings in order to minimize energy loss and operating expenses. (Source: [Nature Energy](https://www.nature.com/nenergy).

These technologies can also be used in industrial settings, and here AI will find those production processes that consume large amounts of energy and advise on how the consumption can be brought down.

 

Adoption of AI & ML in Varies Renewable Energy Sectors

Solar Energy

Some of the applications of AI and ML in the solar energy sector are related to the optimization of photovoltaic systems. Through machine learning it is possible to forecast the position at which the solar planes should be tilted in order to capture the most energy throughout the day. (Source: [Scientific American](https://www.scientificamerican.com).

In addition, with AI it is possible to take data of the solar farms and spot the panels that need repair and those that are likely to break down soon hence cutting on time and costs.

In 2023, in Wind power Engineering & Development, it posited that; For wind energy specifically, AI-driven predictive maintenance is important.

The collected power data including values of wind turbines helps to identify discrepancies and probable part failures and thus, minimize downtimes due to unscheduled breaks. Source: [Windpower Engineering & Development](https://www.windpowerengineering.com).

Further, the use of AI can also help in the placement and functioning of the turbines in the best way concerning the wind flow and velocity.


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Hydropower

In hydropower, applications of AI and ML include the efficient control of water flows and the proper efficiency of the turbines. Predictive models can forecast water availability and energy production, allowing operators to plan more effectively (Source: [Hydro Review](https://www.hydroreview.com).

AI can also help in maintaining the health of hydropower plants by analyzing data from sensors and predicting equipment failures (Source: [International Journal of Hydropower & Dams](https://www.hydropower-dams.com).

 

Biomass

On the processes of converting biomass energy, AI and ML contribute towards making the processes efficient. Machine learning models can predict the optimal conditions for biomass conversion, improving yield and reducing waste. (Source: [Biomass Magazine](https://www.biomassmagazine.com).

AI can also help in supply chain management, ensuring a steady and efficient supply of biomass materials. (Source: [Bioenergy International](https://www.bioenergyinternational.com).

 

Customer Cases & Findings

AI and ML in renewable energy technologies are implemented successfully by the leading companies and research institutions, revealing the improvements and case studies.

Google DeepMind, Driving Value Collaboration with National Grid of the United Kingdom.

The following is an example of DeepMind which is the involvement, Google’s DeepMind to the UK National Grid.

This partnership’s main objective was to enhance the reliability of renewable energy production.

Using the machine learning models has highly enhanced their ability to predict the wind energy more accurately.

This has made the integration of the wind power into the grid more efficient hence enhancing energy distribution other than utilizing non-renewable sources of energy (Google DeepMind, 2021).

Tesla’s use of Artificial Intelligence in Management of Energy

Tesla is another company that pioneered the use of AI renewable energy as can be evidenced by their products.

The utilized artificial intelligence focuses on the improvement of energy management in solar products, and battery storage systems.

These systems incorporate data from the operation to improve efficiency and reliability with the utilization and storage of energy. Thus, the innovative approach of Tesla has positively impacted the efficiency of their renewable energy solutions boosting the improvement of the energy industry. (Source: https://www.cleantechnica.com).

 

Challenges and Limitations

However, there are also some issues and drawbacks that require further discussion concerning the usage of AI and ML in renewable energy. Technically, the integration of AI systems into existing energy infrastructure can be complex and costly.

To develop a reliable and accurate forecasting model of AI, it is also necessary to focus on the aspect of minimizing errors that can be vital in their consequences. Ethically, the deployment of AI raises concerns about data privacy and the potential for bias in algorithmic decision-making.

Moreover, regulatory hurdles can impede the widespread adoption of AI technologies in the energy sector, requiring updated policies and standards Source: [Energy Policy Journal](https://www.elsevier.com/locate/enpol).

 

Future Prospects

AI and ML applications in renewable energy have a very bright future, and the future seems to contain more improvement and new discoveries in this field. Quantum computing and better neural networks are some of the imminent technologies that have the potential of improving AI systems.

Additionally, the increasing availability of big data and improvements in data analytics will enable more accurate and efficient AI models.

As AI and ML technologies continue to evolve, they are expected to play a pivotal role in the transition to a sustainable and resilient energy system Source: [Nature Energy](https://www.nature.com/nenergy).

 

Conclusion

Therefore, when applied to the renewable energy sector, Artificial Intelligence and Machine Learning can prove to be effective in several ways such as the aspects of predictive analysis, optimization of the storage and distribution of energy and improving energy efficiency.

Though, there are limitations and issues to be faced, the use of AI and of ML can change and improve renewable energy systems.

With each passing year, the impacts of technology are seen in the advancement of AI and ML and thus, their future will be instrumental in extracting the necessary progress in renewable energies towards a reliable and efficient energy system.