One of the biggest threats that humanity currently faces is that of climate change. With the number of climate-change-related issues resulting in more and more deaths each year, the challenge before governments and energy solution providers is to bring out a sustainable mode of renewable energy.
With key industry players constantly looking for means to cater to the burgeoning demand for clean, cheap and reliable energy, emerging technologies like artificial intelligence and machine learning have become the solution to this industry woe.
In this article, we look at how renewable energy providers are currently using AI and ML to improve their functioning.
Weather Forecast
One of the main challenges which have been attributed to renewable energy sources like wind and solar energy has been the intermittence in connection leading to unsteady power connections. Be it lack of sunlight due to a cloudy day or drop in the wind speed, this could substantially affect energy generation.
To overcome these challenges, companies are tapping to AI to develop models and software to predict change in weather patterns.
Google’s DeepMind recently announced that it is working in this field. According to the company, by training its neural network with the widely available weather forecast, combined with turbine data, to improve the efficiency of wind energy by 20 per cent.
By doing so, the system could predict wind power output 36 hours ahead of the actual generation. further, they trained the system to make optimal hourly delivery commitments to the power grid a day in advance based on the predictions.
“Although we continue to refine our algorithm, our use of machine learning across our wind farms has produced positive results. To date, machine learning has boosted the value of our wind energy by roughly 20 per cent, compared to the baseline scenario of no time-based commitments to the grid,” the researchers said in a blog post.
Monitoring The Health Of The AI Systems
The future of renewable energy will be shaped by autonomous and robotics technologies. These emerging technologies are increasingly being used to automate operations and to boost the efficiencies of devices like solar panels and wind turbines.
DNV GL – Energy. one of the leading players in the field is now looking at leveraging AI to improve their product offerings. According to the company, autonomous drones with real-time artificial intelligence can be used to carry out effective and efficient inspections of wind turbines and solar panels. While robotics can play a vital role in remote inspection, and proving to be more beneficial in maintenance and troubleshooting.
“We expect the installation of more sensors, the increase in easier-to-use machine learning tools, and the continuous expansion of data monitoring, processing and analytics capabilities to create new operating efficiencies—and new and disruptive business models,” commented Lucy Craig, Director Technology and Innovation at DNV GL – Energy.
Designing Renewable Energy Systems
As renewable energy is a data-rich environment and with wind turbines and solar panels generating data periodically, more and more industry players like
DNV GL – Energy and NREL energy are now embedding AI in their systems. Various aspects of the technology like machine learning and automation are being used for real-time monitoring, supply chain optimisation and even for cost reduction.
Managing Pricing
Due to the high rate of unpredictability associated with the renewable energy sector, AI has been used to make predictions about the demand by leveraging smart meters to foresee energy usage among users. Further, by using machine learning on meteorological data, the outcomes can be used to predict the energy production in the future thus substantially reducing the cost.
Recently in India, two researchers from the Thapar Institute of Engineering and Technology, designed a cost-effective and time-efficient AI to inspect solar panels. The device uses machine learning and clustering-based computation for a speedy inspection process, thus bringing down the cost of energy production substantially by increased solar power forecasting models.
