Energy forecasting: How can we predict renewable generation?
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Over forecasting and under delivering may be a thing of the past for suppliers with the help of machine learning
Rain or shine, weather is a constant topic of conversation. Whether we’re eager for predictions of a sunny weekend to be spot on, or hoping that forecasted rain never comes, it’s fair to say that weather forecasts have an impact on our lives and behaviour. Nowhere is this more true than in the renewable energy sector, where accurate forecasting has an important role to play.
Many forms of renewable energy are reliant on particular weather conditions, so being able to predict these, and therefore production levels, is vital. At the moment, renewable energy sources make up around 26% of the world’s electricity, but this share is expected to grow to 30% by 2024 and global supply of renewable electricity could expand by up to 50% in the next five years.
As we become increasingly reliant on renewable power, forecasting becomes only more important in predicting supply and therefore balancing demand. Failure to forecast production accurately can lead to unusual spikes and dips in price for suppliers and so these are carefully tracked using forecast accuracy metrics which allow system operators to anticipate potential shortfalls.
Variable renewable energy resources, such as wind and solar, must be monitored particularly closely. This is done by inputting weather data, like temperature and pressure, into weather prediction models which can estimate production levels for specific sites. This then allows system operators to anticipate production fluctuations for not only intra-day, but day-ahead scheduling too. But this remains an area where there is room for improvement, so forecasting methods are almost constantly being assessed and improved.
New methods for predicting generation using machine learning
In recent times, there have been rapid developments in the world of machine learning and AI, and these methodologies are already showing great success in generation forecasting for renewables.
In the US, DeepMind and Google have shown great results applying machine learning algorithms to forecast wind power output. Whilst in the UK, the National Grid has turned to AI in order to improve forecasting for both wind and solar. Joining forces with The Alan Turing Institute, these new AI predictions have delivered an impressive 33% improvement in solar forecasting.
Limejump too recognises that AI is becoming more and more prominent in the energy industry, and this is an area of constant research and development for our team of data scientists, who are using more and more machine learning techniques in order to improve forecasting performance for Limejump’s customers. In order to predict generation and use accurately, and therefore make the entire system of energy delivery as efficient as it can be, developing sophisticated AI systems is vital.
What does accurate forecasting mean for energy prices?
Forecasting, and particularly forecast uncertainty, has a huge impact on energy price volatility. As price volatility essentially always comes down to a question of supply and demand, being able to better predict generation levels, or supply, should reduce the number of spikes we see in pricing.
At the moment, over forecasting is a particular problem as when renewable sites under deliver, it can create something of a scramble to reach balance across the system. As renewables continue to grow at such a pace that they could soon particularly in light of the low demand we’ve seen recently, oversupply demand in the UK at certain points, forecasting accurately will only become increasingly important.
Storage solutions will become more vital too as with a changing generation mix, new ways to maintain system balance will have to be found. With accurate predictions for generation, suppliers should be able to measure, ahead of time, generation in relation to predicted demand and store excess power in order to avoid negative pricing runs.
How can Limejump help suppliers?
Limejump is a flexible energy company and as a result holds many values which can be of huge benefit to its suppliers. In the fight against climate change, Limejump is committed to the use and development of AI, machine learning and new technologies to make the energy system as efficient as possible across the board – this means that suppliers should be able to avoid negative pricing for their output.
Bespoke algorithms are already in place at Limejump to intelligently forecast suppliers’ generation and therefore make sure assets are managed with potential output in mind. Online tools also allow suppliers to adjust expected generation, for example to schedule planned outages, in order to adapt forecasts and planned output.
All this is possible through Limejump’s power purchase agreements (PPAs) which work perfectly for variable renewable assets such as wind and solar. Track and trade price PPAs, rather than fixed term agreements, allow suppliers to use the market volatility often created by current deviations in forecasting to make the most of their energy assets. These work by delivering the true value of your product at the time of sale, rather than a fixed value calculated at the time the agreement was drawn up.
For any energy supplier, accurate energy forecasting is of paramount importance and, fortunately, such predictions are improving rapidly across the board. In the meantime, Limejump provides a bespoke service for suppliers who aren’t afraid of a little uncertainty.