- What are the possible ways that AI can facilitate the transition to renewable energy sources?
- When the weather becomes a fascinating topic of discussion
- What are the potential risks of using AI in renewable energy?
- Are you looking for a software team that also wants to build the future on greener energy?
What are the possible ways that AI can facilitate the transition to renewable energy sources?
The recent emergence of AI chatbots and other types of AI-based tools resulted in an interesting tendency in the software industry. People realized how valuable those tools can be and how impactful and game-changing AI can be in general. If there’s a new software idea, there’s a high chance it will somehow utilize or connect to AI.
The same goes for all the technologies related to renewable energy. AI can potentially be used in every phase of renewable energy production - from finding the best locations and positions for solar panels and energy farms to day-to-day management, maintenance, and even optimizing energy consumption. So, let’s move on to the specifics.
When the weather becomes a fascinating topic of discussion
I’m sure people responsible for weather forecasts are doing the best they can with the tools they have. Unfortunately, it’s fair to say that they’re failing quite often. And when it comes to the future of energy, we really can’t afford such a low-reliability standard.
Whether we like it or not, a vast majority of all the crucial challenges of renewable energy are related to weather and climate, which are insanely difficult to predict with high accuracy. That’s why the better we can understand climate change and predict the weather, the more efficient we’ll be in producing clean energy. AI can be a massive help in that aspect because it allows us to analyze much more data much faster.
The platform IBM Watson Studio, developed by The Weather Company, is an excellent example. It helps utilize artificial intelligence to process all types of current and historical data and predict the weather more accurately than traditional methods. Furthermore, it can be used to analyze climate change and create predictions for years to come.
Another company that focuses on that subject is DeepMind. In 2020, this Google-owned company published a report explaining the use of machine learning to predict weather more accurately.
How AI helps with wind farm and solar farm planning?
It is natural to think of AI's assistance in determining the optimal location for a solar or wind farm. Factors such as solar irradiation or wind speed are crucial in this regard, but they are not the only ones that contribute to financial success.
Imagine if we could integrate this data with information about the number of competitors nearby or the historical projects that failed, and more importantly, the reasons behind their failures. Additionally, we could estimate the optimal route for connecting power-generating assets to the grid and develop an initial cash flow model. Ultimately, this would allow us to receive a clear recommendation as to whether continuing the project in a given location is viable or not.
Smart production, smart consumption
Most of the renewable energy sources work in cycles. Whether that’s day and night, sunny and cloudy days, we have to be aware of those cycles. So, if we want to be efficient, we should be able to maximize energy consumption while our solar panels and wind farms are producing the most energy.
The strategy is already highly utilized in modern industrial facilities, but hopefully, we’ll be able to make it work in our houses thanks to apps that suggest, for example, when to charge our electric car based on accurate weather reports.
If we can’t utilize energy, we should be able to store it
Products like Tesla Powerwall or GE Energy Storage allow consumers to gather the excess energy that’s not being used at the time. But there are ways to elevate this idea to a whole nother level.
One of our clients is working on a technology that aims to utilize electric cars the same way - as energy storage. Right now, the solution is being tested on the fleet of electric buses. When they’re not used, they have more than enough time to recharge batteries, so they can also be utilized as storage for excess energy.
In theory, the technology can also be utilized on a much smaller scale - in households that have one or more electric cars. That way, families could save a significant amount of money on energy. Of course, there are many more ways to store the excess energy, but the entire idea is to maximize the efficiency of how we consume energy and avoid any losses. The understanding and cooperation between everyone across the energy grid can make this possible, and AI can play a significant role on the analytical side of things.
Weather isn’t the only thing we need to predict
Here’s a little scenario:
We’ve just learned that in the next few days, we’re expecting strong winds almost non-stop right where our wind farm is. Fantastic news!
But wait a moment. Two of the turbines are malfunctioning and had to be turned off until repair. And there’s no way to repair them right now. That means we’re about to lose massive amounts of energy. If only we had a way to prevent or predict the failure before…
Well, there’s a way. It’s called predictive maintenance. Thanks to IoT sensors and AI-supported analysis of different types of current and historical data, modern manufacturers and other companies learn to predict failures before they happen to avoid downtime and maximize efficiency. The sufficient implementation of predictive maintenance in wind or solar farms can make a world of difference. Moreover, with accurate weather predictions, we’ll be able to plan maintenance during cloudy or windless days.
What are the potential risks of using AI in renewable energy?
When we’re talking about the massive potential of AI, we need to remember that it’s still just a tool. And just like with any other tool, if we’re not careful enough, we can hurt ourselves.
The biggest risk comes down to the well-known phrase “garbage in, garbage out.”
AI can make predictions based on the available data. So, here’s where things can go wrong:
- Some sensors are not functioning correctly, and they provide inaccurate data.
- There is not enough data from certain areas, which gives an incomplete picture.
- We don’t know everything about the factors that cause climate change. There might be relevant aspects that aren’t included in the analysis whatsoever.
- There’s a possibility of foul play. Someone can deliberately mess with the key pieces of data.
While we’re developing AI-based solutions and we’re starting to include AI analysis in decision-making, we still have to pay close attention to the quality of data. Taking care of that part will definitely play a crucial role. But if play our cards well, AI will surely make the transition from fossil fuels to renewable energy much more manageable.
Are you looking for a software team that also wants to build the future on greener energy?
At some point, we’ve decided that we want to do more than just develop good software. We want to contribute to our planet's sustainable future and focus on energy-related software with which we’ve had quite a lot of experience.
So, if you have this type of project, we’d love to help you make it in the best way possible.