The global AI market for clean energy is expected to exceed $75.82 billion by 2030, and the industry’s confidence in this transformative technology is undeniable according to Kyotu Technology. At the moment, the energies we currently use are going to disappear, which is why we need an energy transition via solar, wind or hydraulic energy.
These are the ones that will help us find sustainable, environmentally-friendly solutions. And why is it so urgent to make this transition?
We don’t need to tell you! We can all see how climate change is becoming more and more worrying as fossil fuel reserves run out, not to mention the fact that we’re trying to reduce carbon emissions at all costs.
So what can we do? What if there was a technology called artificial intelligence that could help us? And… what if there was a technology that could help operators, even just a little, to improve the energy optimization capabilities of renewable energy infrastructures?
And…what if there were other technologies that are useful in
Predictive maintenance in renewable energies
Well, we want renewable energies, we demand them, we love them, and that’s normal.On the other hand, there are a few things to sort out when it comes to maintenance.
According to the FDM Group ,the way we do maintenance often means that we have to do inspections all the time, or react immediately to equipment failures.
All this causes unnecessary downtime, messes up the scheduling of interventions, particularly in remote areas or at sea, not to mention increasing maintenance costs.
And since renewable energies don’t operate continuously, because wind speeds can vary or because there’s no daylight, maintenance planning becomes more complicated. So what is
AI’s role in predictive maintenance
According to the FDM Group, these algorithms learn from historical data, identifying patterns and correlations that can indicate whether there are equipment failures about to take place.
If we now switch to renewable energies, AI would be very useful when it comes to analyzing data that comes from sensors embedded in the infrastructure, past performance records and environmental factors, so as to know what problem might occur, and how this will optimize maintenance schedules.
To what extent is AI-driven predictive maintenance used?
In a field like solar energy, it’s used to identify potential problems with photovoltaic (PV) panels. For those who don’t know, photovoltaic panels are the flat surface that captures solar radiation in order to produce photovoltaic energy in the form of electricity.
If AI algorithms analyze data on how well each panel is performing, we’ll be able to detect anomalies such as declining efficiency or deteriorating panels, and know when maintenance is required.
At least, that’s what the FDM Group says. And it’s relevant in the sense that it will guarantee much better energy production and extend the lifespan of solar installations.
There are also wind turbines that need predictive maintenance with AI and especially wind turbines that wear out very easily and not to mention the fact that other components like bearings and gears are not what they used to be over time.
According to the FDM Group, AI can tell when there will be failures by analyzing data from sensors that monitor vibration, temperature and other indicators.
If operators know when this or that component is going to fail, it’s a piece of cake to schedule maintenance activities as if they were anticipating everything that’s going to happen, so they can make downtime as insignificant as it is improbable, but on the other hand it would make energy production so… productive.
Let’s turn the page and tackle hydroelectric systems, where we really need the performance of turbines and generators.
So, as the FDM group would like, we really do need to avoid technical problems such as cavitation (the formation of gas and vapour bubbles in a liquid subjected to negative pressure) or imbalance,
so if we let AI take the lead, it will be able to predict these kinds of headaches if, of course, we let it access past performance data as well as real-time sensor information.
The real benefit here is that we could avoid costly repairs and even more unnecessary downtime.
Challenges and limits: let’s talk
If predictive maintenance is to be effective, data must also be available in large quantities and of good quality.
According to Javaid et al (2022), if AI were given data that was not accurate or unreliable to train on, I can assure you that you would find it hard to believe its predictions.
So, I get the impression that it’s currently a problem for operators to invest in sensors, data infrastructure and AI technologies, because without that, there’s no predictive maintenance.
Energy optimization in renewable energies
The three energy sources we’ve already mentioned (solar, wind and hydro) are currently what’s needed if we want to talk about energy that defends the cause and sustainability.
However, as I said earlier, these energies don’t work continuously. Wind turbines can’t operate in the same way all the time, when wind speeds are constantly changing, and solar panels depend on sunlight, so when there isn’t any? They stop.
So these little moments mean that we have problems when it comes to matching energy supply and demand.
Let me tell you about energy optimization. In a nutshell, it’s a process in which we make operational parameters better, maximizing efficiency and output. And why do we talk about it? Because it’s the central point between the reliability of renewable energies and their ability to compete successfully.
Why is energy optimization so important?
The FDM Group defines energy optimization as the art and science of maximizing the efficiency and output of renewable energy systems.
It involves ensuring that energy production is aligned with demand, adapting to the fact that energies don’t work all the time and that their conditions can change, and ensuring that the energy we produce meets quality standards.
If we go back to our renewable energies, it’s still very important to do so to cope, as I said earlier, with the fact that energies don’t work continuously, so we now have other, more reliable choices and they last longer than what we’re used to using if we think economically.
What’s more, its importance goes beyond the simple fact that it increases efficiency. According to the FDM Group, it has a direct impact on the economic viability of renewable energy projects, making them more competitive in the wider energy market.
In addition, optimizing energy production contributes to the overall stability and reliability of the power grid, and thus fosters a stronger ecosystem for the integration of renewable energies.
Benchmarking AI techniques
The integration of AI techniques, including deep learning, neural networks and predictive analytics, in predictive maintenance and energy optimization, highlights their distinct strengths and applications.
Deep learning
Deep learning is adept at automatically learning the most relevant features from datasets, making it suitable for scenarios where manual feature engineering is difficult.
According to Mansouri et al. (2021), deep learning models, in particular multi-layer neural networks, are capable of capturing complex non-linear relationships within data.
Deep learning models can be computationally intensive, requiring powerful hardware and processing resources. The question is, why? Why are depp learning models so complex? In fact, it’s often because we can’t explain or interpret certain results, and that’s what makes the decision-making process so difficult to understand.
Maybe you didn’t understand this part, but that’s okay, just remember that deep learning is used to study a wide range of data which, let’s not forget, are not eternal,
and it actually comes from wind turbines, so it’s easy to know when you’re facing potential faults or things that are unclear or abnormal in performance, and all this by detecting subtle patterns.
Image recognition tasks, such as identifying anomalies in solar panels through image analysis, illustrate the capability of deep learning in solar energy applications (Mansouri et al., 2021).
Neural networks
Versatile neural networks excel at recognizing complex patterns in data, making them suitable for fault detection and prognosis in predictive maintenance. According to Chen et al (2021), neural networks adapt to changing conditions, enabling them to learn continuously and adjust predictions in line with evolving data patterns.
The effectiveness of neural networks is highly dependent on the quality and quantity of labeled data available for training. Neural network training can be complex and time-consuming, requiring careful tuning of hyperparameters.
Neural networks are effective in fault detection applications, analyzing sensor data to identify deviations from normal turbine performance, enabling proactive maintenance. In wind energy, neural networks help predict the remaining useful life of critical components, facilitating maintenance planning (Chen et al., 2021).
Predictive analysis
Predictive analysis, based on statistical modeling, provides interpretable information on the factors influencing maintenance forecasts, offering transparency in decision-making.
According to Sri Preethaa et al (2023), the use of statistical techniques provides a robust framework for understanding the relationships between variables and predicting future events.
Predictive analysis may struggle to adapt to highly dynamic or non-linear systems, where traditional statistical models may fail to capture complex patterns. The effectiveness of predictive analysis is highly dependent on the availability of historical data, and sudden changes in operating conditions can impact on its accuracy.
Predictive analysis can be applied to estimate the probability of inverter failure based on historical data and environmental conditions.
In wind energy, predictive analysis can be used to efficiently schedule maintenance activities based on historical performance and weather forecasts (Sri Preethaa et al., 2023).
The choice of AI technique depends on specific use cases, data characteristics and operational requirements. Deep learning and neural networks are good in scenarios where complex patterns and non-linear relationships need to be identified.
Predictive analytics, with its interpretive capability and statistical modeling, may be preferred when less dynamic systems are involved and a transparent decision-making process is crucial.
Challenges and opportunities
The fusion of AI and renewable energies has opened up new frontiers in the search for sustainable and efficient energy solutions.
However, this integration comes with its own set of challenges that need to be addressed to unlock the full potential of this transformative partnership.
Data security and privacy
With AI applications in renewable energy relying heavily on the collection and analysis of large amounts of data, ensuring data security and privacy has become a paramount issue. According to Shateri et al (2020), the interconnected nature of energy systems and the transmission of sensitive information pose risks that require vigilant attention.
Growing dependence on interconnected devices and smart grids increases vulnerability to cyber-attacks. Malicious actors may attempt to disrupt energy infrastructures, with potential economic and environmental repercussions.
Granular data collection, particularly from smart meters and sensors, raises privacy concerns (Shateri et al., 2020).
Developing and implementing robust encryption methods and secure communication protocols can protect data during transmission, reducing the risk of unauthorized access.
According to Seth et al. (2022), advances in privacy-preserving AI techniques such as federated learning and homomorphic encryption make it possible to extract valuable information from data without compromising privacy.
Interoperability challenges
The heterogeneous nature of renewable energy systems, combined with various AI technologies, poses interoperability challenges.
According to Rane (2023), the lack of standardized frameworks can hinder seamless communication between different components and systems, thus undermining the scalability and efficiency of AI applications.
The coexistence of various AI models, each developed using different technologies, poses difficulties in creating interoperable systems capable of exchanging information effortlessly.
The lack of universally accepted standards for data formats, communication protocols and interfaces complicates the integration of AI solutions across different renewable energy platforms (Rane, 2023).
Collaborative efforts to establish industry-wide standards for AI applications in renewable energy can streamline interoperability and facilitate the exchange of information between various systems.
Promoting the use of open-source platforms and tools can encourage the development of interoperable solutions, fostering a collaborative ecosystem (Rane, 2023).
Difficulties of integration into existing infrastructures
Integrating AI into existing renewable energy infrastructures poses challenges due to the need to modernize them and ensure compatibility.
According to Yaqoob et al (2023), many renewable energy systems were not initially designed with AI integration in mind, making the adaptation process complex.
Adapting AI solutions to older renewable energy systems, which were not initially designed to accommodate advanced technologies, requires careful planning to avoid disruption and inefficiencies.
Implementing AI solutions can involve high initial costs for infrastructure upgrades, new equipment acquisition and staff training, posing financial challenges for some operators (Yaqoob et al., 2023).
Phased implementation of AI solutions, starting with specific components or subsystems, enables a gradual integration process that minimizes disruption and spreads costs over time.
Designing renewable energy systems with adaptability in mind makes it easier to integrate AI technologies in the future, fostering a more responsive and efficient energy infrastructure.
Opportunities for further research and development
While challenges exist, they serve as catalysts for further research and development, offering exciting opportunities to advance the application of AI in renewable energy. Key areas of opportunity include
1. Developing AI-driven predictive maintenance models that can accurately anticipate equipment failures, optimize maintenance schedules and reduce downtime in renewable energy systems (Ahmad et al., 2021).
2. Research into AI algorithms for real-time grid management, enabling a better balance between energy supply and demand, the integration of intermittent renewable sources and efficient energy distribution (Hannan et al., 2020).
3. Investigating AI techniques to optimize energy storage systems, ensuring efficient charging and discharging cycles and maximizing the utilization of stored energy (Li et al., 2023).
4. Explore AI solutions to manage decentralized energy systems, such as microgrids, to improve energy resilience, reliability and self-sufficiency (Şerban and Lytras, 2020).
5. Advance AI-powered decentralized energy exchange platforms, where individuals and organizations can sell surplus energy back to the grid or trade it with each other, making clean energy more affordable according to Forbes
6. Bridge the expertise gap by encouraging collaboration between AI experts and renewable energy professionals to develop tailored solutions that meet the unique requirements of the energy sector according to Forbes.
7. Improve the quality and diversity of data sources to increase the accuracy and reliability of AI-driven predictive maintenance and energy optimization models according to Forbes.
8. Explore innovative techniques, such as federated learning and homomorphic encryption, to address data security and privacy concerns in the integration of AI and renewable energy (SETH ET AL., 2022).
9. Develop standardized frameworks and protocols to facilitate interoperability between various AI technologies and renewable energy systems (rane, 2023).
10. Design renewable energy infrastructures with inherent adaptability to enable easier integration of AI solutions in the future, creating a more responsive and efficient energy ecosystem (yaqoob et al., 2023).
Conclusion
The symbiosis between AI and renewable energies holds enormous promise for a sustainable and technologically advanced future.
By harnessing the power of AI in predictive maintenance and energy optimization, the renewable energy sector can improve the reliability, efficiency and competitiveness of clean energy solutions.
AI-powered tools, combined with human expertise and ingenuity, can optimize complex hybrid generation projects, seamlessly integrating renewable sources into the power grid according to Forbes.
The integration of AI and renewables offers a future where decentralized energy exchange platforms, powered by AI algorithms, can predict prices, optimize the timing of exchanges and ensure efficient redistribution of energy, making clean energy more affordable and accessible (Forbes).
However, the journey is not without its challenges. Data security and privacy, interoperability issues and difficulties integrating into existing infrastructure require collaborative efforts, standardization and ongoing research.
By answering the call to action, researchers, practitioners and policy-makers can collectively contribute to a paradigm shift in the renewable energy sector.
Through the synergistic relationship between AI and renewable energies, we can pave the way for a sustainable and technologically advanced future, paving the way for a greener, more efficient and resilient energy landscape.
