How to leverage weather data for better energy decisions

Leveraging weather data for better energy decisions 

Renewable energy is a volatile energy source, partially due to its reliance on the weather, which can be extremely unpredictable and only set to increase with climate changes. While we can’t control the weather, we can monitor it and use the learnings gleaned from that monitoring to address supply and demand issues. Energy forecasting with weather data could become the future of supply and demand prediction.  

As renewable energy becomes more widespread, the need for weather-driven energy forecasting is growing as we increasingly rely on it to power our homes and businesses.  

How does weather data enhance renewable energy optimisation?

With the predicted changes in the weather due to climate change, the role of weather data in long-term energy planning is important.  

1. Predicting solar and wind energy generation 

One way to improve weather prediction is via historical data - analysing data we have already gathered from past renewable weather behaviour. We then use that data to predict when we might have bottlenecks or droughts in energy supply compared to energy demand and strategies to avoid this happening in the future. This can only work if we have access to a lot of historical data gathered over a long period, so it’s only applicable to renewable types that have been around for a certain long time, such as wind and solar energy.  

2. Weather-driven energy storage strategies 

When we have periods of energy drought - for example, in the case of solar energy when there are increased storms or periods of cloudy weather - energy storage is must be integrated into the strategy to allow for backup energy during drought periods. This is usually in the form of batteries, which can bank energy during high-production periods - for example, wind energy, when we experience very windy days - and then release the stored energy into the grid during the aforementioned energy droughts.  

3. Reducing curtailment with advanced weather forecasting 

Advanced weather forecasting can also help us to alleviate the issues related to the opposite of energy droughts - energy bottlenecks or ‘flooding’ the grid with energy. This occurs during extreme climate events, such as storms in the case of wind energy, when too much energy is generated, flooding the grid with energy that outstrips demand. The battery storage we mentioned before can help to mitigate and take advantage of the effects of these weather events. Still, sometimes that’s not enough, and we have to introduce energy curtailment, which deliberately prevents renewable energy plants from generating energy.  

Leveraging weather data for demand response, grid resilience and weather forecasting 

Analysing weather data can help us manage energy demand and stabilise the grid so it is more resilient.   

Using forecasts to balance energy loads 

Identifying patterns in historical data can help us prepare for periods of high demand and low supply by stabilising the grid to deal with these unpredictable periods. We can do this by looking at historical demand and supply imbalances, seeing how the grid behaved, and altering the amount of renewable energy generated—or where it is generated from—to meet the demand.  

The future of weather-based energy decisions 

AI and machine learning is predicted to be a critical technology for forecasting weather vs energy demand. If we interface AI with data platforms used to capture plant data, AI could analyse and identify patterns within machine health data as well as consumer data, creating algorithms to stabilise supply and demand and forecast usage during cartoon weather events. It could also be used to give recommendations to human operatives about the predicted behaviour of the grid during certain weather events and how to avoid them destabilising the smart grid.  

The role of IoT and smart sensors in weather monitoring 

It’s not enough that we monitor how renewable energy plants based on how they have behaved in the past in weather-related scenarios; we need to monitor exactly how the equipment behaves during extreme weather events; this allows us to predict factors like machine failure due to weather erosion, or predict when a plant may need to shut down due to an extreme weather event and how this may affect the grid. We can do this via the Internet of Things (IoT): a network of tiny sensors all monitoring the behaviour of each technological component of renewable energy plants. We can also monitor the behaviours of consumers during high-demand periods and analyse how the weather may affect these periods.  

Integrating weather data with smart grid technology 

As renewable energy becomes more widespread, the grid must get smarter to incorporate these technologies and the old. Legacy equipment is famously difficult to integrate into the smart grid, as rapidly advancing technologies leave legacy equipment with connectivity problems. Problems can arise with incomplete data sets due to the incompatibility of legacy and renewable plants, meaning we can’t accurately predict how the weather affects legacy equipment or implement changes to improve this. This is why we must utilise the IoT to monitor the elements of legacy plants that cannot connect directly to the smart grid.  

Leveraging weather data: actionable steps for energy stakeholders 

Weather prediction undoubtedly benefits energy market stakeholders, particularly AI integration. But how can businesses optimise energy use with weather insights? 

As renewable energy becomes a more commonplace energy source, we hope to see more international crossover, with stakeholders benefiting from shared knowledge around the technology and how it can be leveraged for decision making regarding weather. Bespoke AI-based projections will also become more common, particularly in the case of decentralised grids. Stakeholders will aim to make decisions based on accurate pools of data that are more relevant to their specific grid, renewable type, or business needs, which should lead to improved energy efficiency overall.  

Weather data is no longer optional—it’s essential. With AI, IoT, and shared insights, energy stakeholders can make smarter, more resilient decisions for a greener future.