Machine learning in renewable energy might seem like an exotic concept, but the truth is that artificial intelligence can help renewable energy become more reliable. AI in renewable energy can address several drawbacks that stand in the way of greater reliability.
Renewable Energy and Weather Conditions
In many ways, renewable energy is a solid and sustainable solution for the rising global energy demand. Unlike fossil fuels, it is available in endless amounts and it does not harm the environment through carbon emissions. However, there are a few drawbacks as well that pose challenges to its widespread adoption.
For starters, renewable energy depends on solar and wind power, which can fluctuate according to weather conditions. Both of these power sources can generate energy only under good weather conditions. But if the weather conditions are not favorable, then there will be no energy output from wind and solar renewable sources. Therefore, changes in weather conditions have a powerful effect on the performance of these two sources.
The sporadic nature of solar and wind power means that the modern economy cannot rely exclusively on them for electricity generation. So although renewable energy in the shape of solar and wind energy can help reduce our dependence on fossil fuels, they are still not reliable enough to act as replacements. Machine learning and AI are the tools that can improve the efficiency of renewable energy sources and increase their share in the power supply.
How Machine Learning and AI Assist Renewable Energy
Machine learning and AI can help with energy management, which involves balancing demand with supply. Under the current scenario, strong energy management driven by AI and machine learning is necessary for maximizing the potential from renewable energy.
Here is how AI and machine learning can improve energy management. During sunny days, solar energy can satisfy much of the demand for electricity. AI and machine learning platforms can, thus, activate solar plants and instruct coal-fired plants to reduce their electrical energy output.
Although weather forecasts are not an entirely new idea, AI and machine learning can help improve these predictions. This improvement is highly necessary since weather predictions can often be unreliable. Quick, accurate and precise weather forecasts can help improve the performance and function of solar and wind power systems.
Machine learning and AI platforms can be fed vast amounts of meteorological data to improve their weather forecast capability. Thus, intelligent systems can predict weather conditions well in advance and signal to coal-fired stations how much energy they will need to produce to satisfy demand. Accurate forecasts and requirement predictions can help the operators of coal-fired power stations to vary their output, according to demand and operate more efficiently.
Machine Learning for More Accurate Predictions
Countries like Germany and other European countries are already using AI and machine learning systems to accurately predict meteorological conditions. This data helps them to anticipate how much electrical energy their solar and wind power plants will be able to generate. These systems can also follow the rise and fall in demand for electric power supply. By accurately predicting both demand and supply for renewable energy in real-time, AI and machine learning systems can help improve reliability and bring down costs.
IBM is assisting renewable energy companies with energy management through its AI systems. The sophisticated platform amalgamates data from thousands of satellites and weather stations to make accurate weather predictions. The system is capable of assessing which weather models can give the most accurate predictions under prevailing conditions. As a consequence, AI-based weather forecasting is proving to be more accurate and reliable than conventional weather forecasts.
Besides helping to improve energy management, AI systems can also help to mitigate the impact of electricity blackouts. The power grid is the lifeline of the modern economy. There is hardly anything that does not depend on the power grid in today’s world. Factories, homes, hospitals and offices all need reliable electric power for steady operation. However, the power grid is not foolproof. It has certain vulnerabilities that can lead to power outages like equipment failure, operator errors, lightning strikes and other factors. A single electricity outage can easily lead to tens of millions of dollars in damages for the biggest companies.
IoT and AI for Renewable Energy
With AI and machine learning systems in place, it may soon be possible to get advance warnings of impending power failures. The smart grid of the future will have millions of IoT sensors and devices that can continuously feed it with data in real-time. Thus, if the conditions associated with electricity outages arise, then the system will be able to provide advance warnings to mitigate the impact of the electricity blackout.
These very same sensors and IoT devices can also help intelligent platforms to assess how much solar and wind energy they can utilize to meet the trends in demand. Smart grids can also help to reduce the cost of maintenance and enable renewable energy systems to optimize supply, according to fluctuations in demand, especially that which is seasonal in nature.
Another unlikely way that AI systems can help renewable energy is through power savings. Modern devices equipped with IoT can reduce their energy consumption. Operators can, thus, reduce the dependence on coal-fired plants and rely more on renewable energy, helping it to achieve its objectives of less emissions and less fossil fuel consumption. Energy savings brought about by AI can help renewable energy systems to consolidate their position in the modern world.
There are numerous ways in which AI and machine learning can assist renewable energy to increase its competitiveness with respect to fossil fuels. Renewable energy is vital for a sustainable environment and energy supply. AI and machine learning are proving to be invaluable tools for helping renewable energy to mitigate its drawbacks, especially its fluctuations with respect to weather conditions. With machine learning and AI, electricity utilities will be aware of how much electrical energy is available from renewable energy. Thus, they will be able to scale their coal-fired operations accordingly and in doing so, reduce our reliance on fossil fuels.
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