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BOOSTING RENEWABLE ENERGY WITH AI AND MACHINE LEARNING

The influence of AI (Artificial Intelligence)

  • The automation potentiality of AI can drive operational brilliance in many key areas.
  • The powerful and precise prediction capability of AI will lead to asset management and better forecasting.
  • Artificial intelligence (AI) has the strong capability to drive the huge potential of renewables. 

An overview of Renewable Energy sources

However, due to the depletion and high consumption of conventional energy the sources like crude oil, natural gas and coal have environmental effects of the burning of fossil fuels. Hence, we can see that the companies and governments are focusing increasingly on developing renewable energy sources. Wind and solar are renewable sources that have been extensively used in recent years. Hydro power is also a renewable source that has been constantly used for many decades. Moreover, their energy production capacity is not fully predictable, due to which we have to ensure use of proper management techniques and forecasting for even and better integration with the power grid. 

 

Increasing research and development to strengthen AI’s capabilities

AI and other emerging technologies, like IoT, big data, sensors etc. are a big challenge for the renewable energy sector. It has been seen that key accelerators such as prediction capability through asset management and demand forecasting are already leading to better yields, high cost savings and better returns on investment as they offer increased automation providing operational excellence.

R&D in AI has the capability to lower costs considerably. Its capabilities should grow more as R&D in the solar industry has pushed down prices. Governments are realizing this and following it, the UK is funding several new research hubs that will be created to develop robotic technology to improve safety in offshore wind.

Machine Learning Makes Renewable Energy More Sustainable

Machine learning strongly helps in predicting many of the relevant factors affecting renewable energy due to which renewable energy sources can become cheaper, more reliable and more desirable. Machine learning is also promoting sustainability in many industries by turning renewable energy into a feasible alternative to fossil fuels. This leads to rapidly bypassing fossil fuels and renewables. 

 

How AI and machine learning can support the renewable energy transition

By predicting both demand and supply, machine learning and AI methods can help renewable energy sources to become as reliable as fossil fuels.

According to research, the share of solar and wind energy in the global power supply will need to more than triple by 2050. Therefore, a high need to match supply to demand precisely. As a result, implementing machine learning and AI to renewable energy could be the most transformative implementation of technology.

In order to forecast the output of renewable energy sources, there is a need for precise knowledge of how that weather will interact with solar panels and wind turbines along with precise weather predictions. Experts say, to help the world achieve carbon neutrality by 2050 there should be training of machine learning models on an expanding source of satellite imagery, datasets and climate physics.

Moreover, machine learning to predict the weather

Current power grids were basically designed for electricity derived from thermal plants, powered by nuclear energy or fossil fuels. This kind of energy can provide a specified amount of power on demand. However, Energy derived from renewable sources such as solar and wind does not basically produce a predictable output as both are affected by the different behaviours of the weather.

Researchers seeking to boost the share of renewables within the energy mix have a clear goal to make solar and wind power more ‘dispatchable’ for which they are trying to make output predictable, so that grid operators have an ease in knowing how much power should be feed into the grid and how much should be stored for future use.

 

Forecasting energy demand with AI

To make renewable energy more ‘dispatchable’. There is a requirement for accurately forecasting demand which includes knowing how much electricity will be required. This will allow grid operators to make the best use of the energy mix.

OVO Energy is a UK-based provider that has a goal to reach carbon neutrality by 2030, using machine learning to forecast energy demand as well as supply. A company that buys energy from both fossil-fuel and renewable sources and sells it to the retail market. 

Their step by step working includes 

  1. understanding of the physical parameters of the building, e.g. how leaky it is and how well it stores heat, and then build predictive models by using machine learning accordingly.
  2. The engineers can then compare this data against a forecast of when there will be sufficient green energy on the grid. With such two forecasts, engineers can then start to plan when to deliver that energy to the heating system, in order to coincide with periods when there’s the most renewable energy on the grid.
  3. Within a day, the price moves around quite a lot. So there is a need to continually forecast and reforecast the price to plan how much a customer should be charged based on the current/ongoing market price of the energy using the market data that’s available. This will help in honing precise prediction.

Energy providers who are also using machine learning to fit the needs of individual customers hence, Machine learning helps them to make the best use of renewable sources.

 

The Future of Machine Learning in Energy

 AI and Machine Learning have huge implications in the energy sector. Developed countries which are looking forward to a completely green economy, maintaining a balanced and reliable power grid is crucial. However, Smart grids are basically power grids which combine the power of IoT (Internet of Things) and AI to form a digital power grid that could enable a two-way communication between utility companies and consumers. Smart grids are equipped with sensors, smart meters and alerting devices which continuously collect and show data to consumers to help them improve their energy consumption behaviors. Machine learning algorithms could be used to improve performance and prevent system failures.

 

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