Applications Of Artificial Intelligence (AI) In Renewable Energy

Hello guys, welcome back to our blog. Here in this article, we will discuss the applications of artificial intelligence (AI) in renewable energy, AI approaches in wind, hydro, solar, and bioenergy power plants, and the significance of artificial intelligence.

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Applications Of Artificial Intelligence (AI) In Renewable Energy

What is Artificial Intelligence?

Artificial intelligence is the process of harnessing machines with human intelligence so they can perform human tasks with human-like precision for known operations. There are several competing views within the field of artificial intelligence (AI), but recent developments in machine learning and deep learning are causing a major breakthrough across the whole technology industry. 

The four approaches define artificial intelligence as adaptive machines, self-awareness, conceptualization, and restricted memory. 

Significance of Artificial Intelligence

To achieve a goal, artificial intelligence should think critically and then behave in ways that maximize the likelihood of success. Machine learning (ML) is a subfield of AI that centers on the idea that computers can pick up new information and improve themselves without any help from humans.  

Deep learning (DL) is a kind of automated learning which can take in and make sense of vast volumes of unstructured data including text, photos, and videos. The prominence of artificial intelligence lies in its capacity for automatic, repeated learning and discovery through data, its ability to lend intelligence to preexisting products, its flexibility through revolutionary learning methods, its ability to analyze more and deeper data, and its astounding accuracy in archiving its information.

Role of AI in the renewable energy system

Renewable energy (RE) is very volatile since it is derived from natural phenomena like the sun, wind, and water, all of which are subject to changes in the weather. Integration and regulation of RE into the electrical system are hampered by its intermittent nature. 

AI may aid the renewable industry in several ways, including predicting the weather, modeling and parameter estimation, line loss predictions, maintaining system reliability, integrating hybrid solar and battery systems, predicting equipment failure, grid management, maintenance, cyber security, and applications in the operation and maintenance of PV farms. 

In weather forecasting, AI helps in accurately predicting the weather by monitoring the current and historical data. Power firms may better manage their energy systems with the aid of this forecast data. However, there are still certain hurdles that must be overcome. Distributing and managing renewable energy supply is now possible with the help of AI and ML technology. AI enables the following features in renewable energy and transformation sectors, such as

1. Build smart control centers: Simultaneous data processing from various sensors integrated with system components.
2. Integrated microgrids- Helps integrate microgrids and manage distributed energy with bidirectional power flow.
3. Improved safety and reliability: Managed the intermittency and offered improved safety, efficiency, and reliability.
4. Expand the market: It introduces new service models and encourages higher participation. 
5. Build intelligent storage units: AI with intelligent energy storage enables a sustainable and reliable solution to the RE industry.

Applications of AI in various RE-based power generation systems

Renewable sources of energy, such as solar, wind, geothermal, and hydropower plants are popular due to their low cost and strong demand. 

AI approaches in the wind energy system

1. Forecasting Wind Speed and Velocity
2. Configuration of Wind Power Plant
3. Interpolation of unavailable wind data
4. Recognizing problems with wind turbines
5. Risk optimization in wind energy trading

AI approaches in the solar energy system

1. Solar radiation forecast
2. Evaluation of solar water heating system performance
3. Forecast daily ambient temperature
4. Solar tracking
5. MPPT of PV array
6. Solar system optimization

AI approaches in the geothermal energy system

1. Prediction of vertical ground coupled heat pump system performance
2. Prediction of static formation temperature
3. Geothermal energy forecast
4. Geothermal mapping production
5. Ammonia-nitrogen prediction
6. PID controller performance estimation
7. Computation of a geothermal plant
8. Prediction of pressure and temperature in a geothermal plant

AI approaches in hydro energy 

1. Temperature prediction
2. Modeling of precipitation-runoff
3. Streamflow forecasting
4. Power discharge estimation
5. Automatic generation control

AI approaches to ocean energy 

1. Forecasting sea level changes
2. Wave parameter forecasting
3. Forecast dispersion coefficient
4. Minimizing the effect of ocean wave

AI approaches in bioenergy 

1. H2S and NH3 detection in biogas
2. Biogas methane concentration forecasting
3. Prediction of biodiesel qualities
4. Biodiesel engine performance forecasting
5. Optimization of the biomass supply chain
6. Prediction of a higher heating value
7. Control of a biomass boiler
8. Optimization of biogas production

AI approaches in hydrogen energy 

1. Forecasting of hydrogen vehicle emissions
2. Error detection in fuel cell
3. Voltage and temperature prediction for the cathode
4. Hydrogen engine features, specifications, and emission forecasting
5. Prediction of energy density
6. Hydrogen usage control

Global renewable energy management industries

RE companies are exploring ways to leverage AI and improve efficiency and accessibility. Some of the major renewable energy sectors worldwide are listed below which incorporate AI and ML for energy forecasting, energy efficiency, and energy accessibility. 

1. Nnergix(Barcelona) 
2. Xcel (Minneapolis)
3. Google Deepmind
4. Verv (London)
5. Power Scout
6. Verdigris Technologies

This was about “Applications Of Artificial Intelligence (AI) In Renewable Energy“. I hope this article may help you all a lot. Thank you for reading.

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