It’s no secret that conventional approaches to electricity production have taken a massive toll on the planet. It’s also clear that fossil fuels won’t last long enough to meet the increasing energy demands of the future.
This necessitates alternative sources of energy – like solar power and wind power. But the energy demand is growing exponentially every year, and most systems are prone to get overwhelmed because of inefficiencies in the system. Despite the increasing use of renewables, there are persistent barriers to wider implementation related to technology and efficiency.
But we have a new tool to help us improve energy generation, manage the impacts of climate change, and protect the planet: artificial intelligence.
Artificial intelligence can wade through volumes of data silos and unstructured documents from different sources to provide valuable insights that could otherwise take forever for human operators. Careful use of AI can ensure a smooth flow of power from generation to consumption in grid operations.
AI can help us forecast the production of energy, predict the weather, manufacture ‘clean’ solar panels’, and establish smart grids, among others.
Here are 7 applications that AI will improve in the renewable energy sector.
Table of Contents
Weather Predictability
Power sources that depend on the state of the weather, such as wind and solar, often get disrupted because of small changes in the climate. Unpredictable weather is one area where most energy consulting firms in the UK are struggling to cope with. But AI could play a central role in addressing the issue of unpredictability.
In the past, AI has helped researchers achieve an accuracy of between 89 to 99 percent in identifying atmospheric rivers, tropical cyclones, and weather fronts, all of which can make energy production difficult.
Renewable energy that depends on the weather is prone to such vulnerabilities. Every now and then, the generation of renewable energy falls short of demand and communities cannot depend on them as a baseload for consumption. Businesses continue to depend primarily on conventional sources of power generation, which makes the transition to renewable energy pointless.
The most popular method used for energy production prediction is the Time Series model. For instance, it can help in predicting the behavior of temperature: heat flux, heat flow, and thermal conductivity. The model continuously refreshes and updates the data to unravel important patterns. Time series forecasting utilizes techniques such as moving averages, autoregressive, and vector autoregressive, to improve the predictability of energy output from sources based on past observation.
Create a ‘Smart’ Grid System
The term ‘smart grid’ applies to more than just the smart delivery of power. For maximum efficiency, a smart grid system requires artificial intelligence and distributed generation.
Most power plants in the world are not built to accommodate the diversification in energy sources, especially not the use of renewable resources. Our current grid systems are woefully underequipped to deal with increasing power demand.
But when coupled with AI, the new influx of data can give valuable insights to grid operators for better control operations. It offers flexibility to energy supplies to easily adjust demand and supply.
For example, advanced load systems can promptly switch off when the power supply is low and the storage units cleverly adjusted based on the flow of supply.
Moreover, advanced sensors and smart machines can make load and weather predictions that can improve the efficiency and integration of renewable energy.
Improves Microgrid Energy Efficiency
A hybrid microgrid uses every resource under the sun, from solar and wind energy to diesel generations, tidal power generation, power-to-gas, and even battery storage, depending on the needs of the town. Such a complex microgrid needs to be paired with AI to slowly and smartly and efficiently distribute energy to areas that need them the most.
Without AI, these microgrids will quickly suffer from congestion problems and quality issues that would require considerable manual input from grid operators.
AI Diagnoses and Fixes Problems
Microgrids aren’t the only areas that can benefit from technologies like AI and machine learning. AI could play a central role in fixing problems in real-time, at least with issues that don’t require manual repair. This is possible because AI systems can sift through massive amounts of data, study the grid, figure out where failures happen and improve those areas so when a blackout does occur, it becomes easier to repair, this way customers won’t end up without power.
If done right, AI systems could easily manage a micro or macro-grid without any intervention from humans. The only task these AI systems can’t do is manually repair faults with the system.
Energy Forecasting
There is a large inter-individual variability on energy consumption depending on several parameters that are beyond the control of humans. These include seasonal factors, weather conditions, temperature, socio-economic elements and more – all of which create a cascade of confusion for human operators hoping to make sense of the data. This leads to poor energy forecasting and more power cuts that hurt the economy. But AI can handle all that data through sheer brute force.
AI can aid the power industry by supporting systems like planning, forecasting, and controlling. By processing energy consumption data, it is possible for models to reveal patterns and trends, and also predict future energy consumption. Two commonly used models to estimate electricity production are LADES (lasso-based adaptive evolutionary simulated annealing) and RADES (ridge-based adoptive evolutionary simulated annealing).
A prominent example of an efficient AI technique is Artificial Neural Networks (ANN) alongside Expert System Techniques and Fuzzy Logic Systems.
Google is already reaping the rewards of using AI to help them meet their energy needs. Their DeepMind AI not only reduced energy consumption but also the emissions that resulted because of it. DeepMind managed to cool Google’s servers by at least 40%, which also reflected as a smaller energy bill. DeepMind leveraged neural networks for over two years, using a set of data center parameters and operating scenarios.
Manufacture Efficient Solar Panels
Conventional manufacturing techniques of solar panels require the use of rare earth elements and excessive temperatures exceeding 2000˚C, which in turn, requires fossil fuel-generated power to achieve. This is ironic given how solar panels are created with the primary intention of alleviating environmental burdens on the planet.
AI can change this, however, by speeding up research into the development of new materials that can create more efficient solar panels. Without AI, researchers will have to go through giant heaps of colossal data requiring thousands of individual tests before finding a major breakthrough. With AI to do all the heavy lifting, it becomes possible to pursue complex trials in a much shorter period of time, unraveling valuable insights, and speeding up the manufacture of next-gen solar panels that can be created without resorting to fossil fuel-based power generation.
Help with Recycling Old Hardware
Recycling solar panels, wind turbines, and other renewable energy apparatus remains a major talking point among experts – how do you get cleanly get rid of hardware that is designed to only last 30 years? One major application of AI will be to discover suitable materials that can be repurposed at the end of their life cycle – and recycled. This will truly help renewable energy become truly ‘green’ and alleviate the planet’s ecological burdens.
Wrapping it all up
AI is poised to play a monumental role in how humans interact with renewable energy. The limitations that currently prevent us from going ‘off the grid’ can be easily tackled by using AI.
Ready to leverage the transformative power of AI in renewable energy systems? Contact our experts here!
