AI And Renewables Website (1)

Harnessing the power of artificial intelligence in renewable energy will lead to greater efficiencies and enable its continued build out.

Many people, when they think about the renewable energy needed to ensure the green energy transition, primarily think of the hardware involved: the wind turbines, the solar panels, and the dams. Whilst of course the hardware is a fundamental component, the software - or more specifically the smart software and artificial intelligence (AI) - will be just as important when it comes to getting the most out of the renewables on the ground.

Supported by other emerging technologies such as state-of-art sensors and the Internet of Things (IoT), AI has the ability to unlock the enormous potential of renewables through demand and supply forecasting, predictive maintenance, and intelligent building controls. A failure by the renewables sector to embrace AI will prevent it from achieving the growth required to meet the global demand and a greener society.

At Dare, we apply machine-learning understanding to the energy markets, looking to maximise the impact of renewables, so we can generate greater efficiencies and competitive advantages. That’s why our teams are so excited by the growing applications of AI in the energy sector, such as:

Demand Forecasting:

Predicting electricity demand is an essential part of the energy market. It allows grid operators to make decisions in power system operation and planning.

The increased use of smart meters means utility providers have more and more data about user demand. AI algorithms can utilise this data and predict network load and consumption habits accurately, thereby controlling costs by keeping the grid supplied at the level required. Using AI, operators can now predict when demand spikes will occur, discharging energy to control the grid and ensuring there are no interruptions.

Supply Forecasting & Grid Stability:

In addition to consumer demand, AI systems can forecast the supply of renewable energy.  Vast amounts of data are used to combine machine learning weather models, historical data sets and real-time measurements, which allows complex variables such as wind speed, rain or sunlight to be modelled. This means we are able to make predictions for short-term renewable power output in minutes instead of days.

By making these predictions, grid operators can control other power plants more accurately, maintaining grid stability and optimising the energy consumption of consumers. Making the most of renewables means less need to burn fossil fuels. For renewable generators, this accurate forecasting means they can understand and model their output better.

System Malfunctions & Predictive Maintenance:

The renewable sector, like many other industries, can have sudden equipment malfunctions or system issues. This can lead to outages and potentially significant business impacts.

With the amount of information available from renewable assets, such as sensors on wind farms or solar panels, data can not only be used for the optimisation of performance but also for the prevention of possible risks.

AI applications can root out system malfunctions in renewable assets by predicting when an error may occur, for example, a wind turbine malfunction or when a power cable needs to be replaced. Predictive maintenance enables renewable assets to be managed effectively and efficiently, pre-emptively fixing issues to reduce downtime. 

Intelligent Building Control:

According to the International Energy Agency (IEA), in 2021 the operation of buildings accounted for 30% of global final energy consumption. [1] However, in many commercial buildings, energy is constantly being wasted. Air conditioning units and lights are often left on whilst buildings are unoccupied.

Using the data from these smart buildings to understand consumption patterns has the potential to reduce energy waste dramatically. By more precisely adapting heating and cooling to when the building’s occupants need them, the building’s electricity load will move from being a more or less a fixed load to a flexible load.

For building owners and operators, using intelligent building controls will help to reduce costs and cut carbon emissions. For users, this can lead to reductions in utility bills. For grid operators this stream of real-time data will enable them to see more predictable flexible demand and so balance supply and demand. This is most helpful as the share of renewable energy increases due to its intermittent nature.

Manufacturing renewables:

Renewable infrastructure like solar panels are near zero-emission when they are installed, but we cannot forget the carbon emitted when manufacturing them, or indeed decommissioning. However, as solar panels have got cheaper to produce, manufacturers have also started looking at ways to make that process less damaging to the environment. [2] Typically, this has involved a lot of trial and error, as new materials and techniques are tested laboriously.

But through intelligent use of AI, R&D departments are narrowing down the field of innovations and deploying the techniques that are most likely to lead to efficiencies, accelerating the journey to lower emission manufacturing. ADA (Autonomous Discovery Accelerator) was the world’s first self-driving AI laboratory and is a fantastic example of putting these ideas into practice.

AI is the key to achieving greater renewable energy efficiencies, enabling its continued build out, and increasing its proportion of the global energy mix. More importantly for consumers and corporates, it will lead to reductions in utility bills at a time when costs are rising in many other areas.

The adoption of AI in the renewable energy market has already begun, but over the next few years it is set to take off as the demand for renewable energy and smart energy solutions grows exponentially. According to Precedence Research, the global market size for AI in renewables is expected to grow from $8 billion in 2021 to surpass $75 billion by 2030, as the potential for AI to transform the renewables industry is realised. [3]


About Dare

At Dare, our mission is balancing the world’s energy to reach a greener future, faster. The transition to net zero is the existential task facing the world today and we are designed to solve one of the most critical challenges of the transition: the intermittency of renewables. We want to use our world-leading technology, market knowledge and trading skills to keep the world’s energy in balance, enabling and investing in the shift to green energy.

[1] IEA – Buildings Tracking Report -

[2] -

[3] Precedence Research - Artificial Intelligence (AI) in Renewable Energy Market -