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, the vast majority of the biggest 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 help a lot here 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 uses artificial intelligence to process all types of current and historical data and predict the weather more accurately than traditional methods. It can also 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.
AI in Wind & 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 matter a lot here, 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. We could also estimate the optimal route for connecting power-generating assets to the grid and develop an initial cash flow model. In the end, we would get a clear recommendation on whether continuing the project in a given location is viable.
Smart production, smart consumption
Most renewable energy sources work in cycles, such as day and night or sunny and cloudy days, and 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 widely used 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 store the excess energy that's not being used at the time. But there are ways to take this idea to a whole new level.
One of our clients is working on a technology that aims to use electric cars the same way - as energy storage. Right now, the solution is being tested on a fleet of electric buses. When they're not in use, they have more than enough time to recharge their batteries, so they can also serve as storage for excess energy.
In theory, the technology can also be used 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 idea is to maximize the efficiency of how we consume energy and avoid any losses. Understanding and cooperation between everyone across the energy grid can make this possible, and AI can play a significant role in the analysis.
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. Properly implemented 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.
