The economic viability and growth of AI in the Energy industry can be attributed to a variety of
reasons, which include:
The demand for improving operational efficiency
Rising interest in energy efficiency
Expanding decentralised power generation
Growing interest in battery storage systems
There are many investment options when it comes to AI in the energy industry, owing to AI having a
variety of applications.
AI and Grid Management:
One of the most interesting uses of AI in energy is grid management.
Electricity is delivered to customers through a complex network (also known as the power grid). The
tricky thing about the power grid is that power generation and power demand must match at all times. Otherwise, issues like blackouts and system failures can arise. When dealing with renewable energy, it is difficult to predict the grid’s electricity production capacity. After all, it depends on several factors such as sunlight and wind.
When large swings in demand occur, it can be very expensive for countries which produce most of
their energy through renewable energy sources. With most countries shifting towards green energy,
responding effectively to swings in demand is becoming even more difficult.
Solving Demand Response:
Aims at forecasting how much wind and solar energy to expect at a given time. This allows the country to make up for excess electricity demand by using non-renewable energy whenever necessary.
In order to accurately match supply and demand, they use large historical data sets to train their machine learning algorithms – as well as data collected from the wind turbines or solar panels – to effectively forecast weather and power changes.
Aside from helping to match energy production with energy consumption, AI is becoming a major driver in assuring the reliability and robustness of power grids.
Where AI and Internet of Things (IoT) Step In:
Machine learning techniques can be used to implement predictive maintenance. In essence, power lines, machinery, and stations are equipped with sensors that collect operational time series data (data accompanied by a timestamp). From there, machine learning algorithms can predict whether a component can fail in X amount of time (or n-steps). Additionally, it can also predict the Remaining Useful Life of machinery, or when
the next failure may occur.
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