• Rhoevine Cabasal

Artificial Intelligence in Renewable Energy Systems: An Overview

Updated: Sep 6

Global energy demands continue to increase every year thus, most of the common energy sources such as fossil fuels further deplete in numbers. Not long down the road, these conventional sources won’t be able to fulfill all the energy demands of the future. Furthermore, the carbon emissions of fossil fuels also rise due to the increasing energy demand. Mother Earth won’t be pleased by this, that’s for sure.


Image by Gerd Altmann from Pixabay


Most renewable energy sources offer safe and sustainable service however, the cost of operating renewable energy systems are still relatively high. The reliability of these systems are also questioned at times since most are dependent on the conditions of natural resources and the environment.


Energy production from different renewable sources differ from each other, thus each source also has its own pros and cons when it comes to energy production. With the aid of artificial intelligence, the disadvantages that arise with the use of renewable energy sources, such as unpredictability of energy outputs, can be lessened and possibly, eliminated completely. Here, we discuss how artificial intelligence has contributed to renewable energy and the advancements that could be in line for the future.


Image by Gerd Altmann from Pixabay



Solar Energy


Solar energy is one of the most recognizable and fastest-growing renewable energy sources. As of the moment, the main application of AI in the solar energy industry is weather forecasting. Since solar panels are dependent on the sun’s projection and the weather, utilizing AI for more accurate weather forecasting improves the efficiency and accessibility of this renewable source.


Image by Sebastian Ganso from Pixabay


In Australia, a solar forecasting vendor, Solcast, employs a third-generation satellite that detects and predicts cloud characteristics. Since the appearance of clouds help predict the weather condition, AI monitors their movement and behavior. It also tracks aerosols and utilizes numerical weather model data. WMDs are those moving images we usually see on television during weather reports. With AI utilizing these data, more accurate predictions are produced.


Future Applications

  • The integration of AI in electrical grids offers a more versatile approach when it comes to adjusting energy supply and demand.

  • Intelligent storage units can be designed according to different factors such as: supply, demand, emergency uses, etc.

  • Improvement of AI software in understanding consumption patterns opens up the possibility of increasing the safety, efficiency and reliability of solar power plants.



Wind Energy


Isn’t it a wonder to look at huge wind turbines that come in different shapes and sizes? In some places, these huge energy-producing equipment even attract a lot of tourists. The main application of AI in wind energy production is the operation and maintenance of the wind turbines. These O & M practices lead to increased efficiency, better spare-parts forecasting, and lower maintenance costs for wind farms.


Image by Matthias Böckel from Pixabay


In July 2019, Australia’s first AI-enabled wind farm became fully operational. Tilt Renewables was responsible for the project while the AI software they used was developed by AMS, a San Francisco-based start-up company. The wind farm was able to positively affect the energy industry when it caused a wave of negative wholesale energy prices in Australia. AI monitors the behavior of the wind turbines and identifies abnormalities by gathering information such as: SCADA (Supervisory control and data acquisition, maintenance data and failure history. Once an abnormality is detected, the wind-farm operator is promptly notified. This simply shows how AI can increase revenue and at the same time, reduce the stress in the national grid.


Future Applications

  • The improvement of AI monitoring in wind turbines can help with fine-tuning automation

  • AI forecasting for weather conditions and equipment health can be enhanced for more optimized operations

  • Automating the operation of wind turbines with the help of AI improves the efficiency and lessens operating costs since the operation may be programmed to be dependent on different factors such as weather conditions, energy demand, etc.



Hydroelectric Energy


Water is the most abundant compound in the planet thus, its contributions to the utilization to the renewable energy industry are more than significant. In 2015, about 70% of all renewable electricity was already being taken from hydropower.


Image by 子墨 黄 from Pixabay


As of the present, the involvement of artificial intelligence in hydropower plants is similar to its function for power and wind farms. However, most hydropower plants still prefer conventional methods over using artificial intelligence in the operation of these facilities.


Future Applications

  • The development of AI softwares that monitor water conditions that affect energy production such as salinity, water levels, etc can introduce automation and a more all-encompassing overview of the facility.

  • Smart seawater batteries that can output sufficient power and store excess energy for sustained usage can improve the sustainability of such plants.



Biomass and Geothermal Energy


Biopower utilizes plant or animal materials for energy production while geothermal energy is basically the energy produced from underground. These two sources are the fourth and fifth biggest renewable power sources, respectively.


Image by WikimediaImages from Pixabay


The use of Artificial Intelligence for these two renewable power sources are also similar to the other sources: mainly monitoring and forecasting. However, the contribution of AI for these two sources can still significantly grow in the future.


Future Applications

  • For instance, smart selection of ideal locations for smaller scale power plants can contribute to further optimizing the generation of biomass and geothermal energy.

  • AI-aided data gathering for smarter biomass acquisition would also help in waste management and energy production.




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