Implementing Machine Learning in the Energy Industry



Artificial Intelligence (AI) and Machine Learning (ML) are both hot topics in business these days. These advanced analytics tools have been introduced into businesses across many sectors – from retail, to finance, to marketing, with one of the less publicised yet important sectors being the energy and utilities sector. 


The power of Machine Learning, data science, AI, and predictive models has made many renewable energy companies more efficient across business functions – lowering costs, increasing returns, and making more accurate predictions. 



Before getting in to how Artificial Intelligence and Machine Learning can benefit energy companies, allow the below section to provide more clarity on the terms.   

Artificial Intelligence (AI) refers to any machine or model that can conduct human-like activities.  

Machine Learning (ML) is related to AI, however they are certainly not the same thing. 

AI refers to computer-generated intelligence in machines; Machine Learning allows for some sort of artificial intelligence by using statistical techniques.  In other words, Machine Learning algorithms allow a machine to train itself based on datasets to perform a task more efficiently. 


Artificial Intelligence and Machine Learning in the Energy Sector 

Below are 3 use cases of these data analytics tools in the energy industry:  


Forecasting Energy Demand  

When it comes to delivering electricity to customers through complex power networks (grids), there needs to be enough electricity or energy available to match the demand for it. Otherwise blackouts and system failures may occur. 


Machine Learning algorithms can determine demand by looking at past data of daily energy consumption changes for individual customers over time. From this data Machine Learning models are able to generate very accurate energy demand and consumption forecasts. These predictions can be used by energy & utilities companies to save time, optimise operations and every storage systems, and prevent customer dissatisfaction.  


Predictive Maintenance for Power Systems 

In addition to matching energy demand with energy productionMachine Learning is a beneficial tool to ensure the consistency and health of power grids. In the past, massive blackouts and power cuts have occurred with no warning to energy companies of upcoming predicted faults in their systems or even warning about the power cuts currently occurring. Machine learning algorithms can implement predictive maintenance to predict when assets (power lines, stations, and machinery) are estimated to fail. These predictions are made from analysing data – collected from sensors on power lines, boxes, and machinery  against a timestamp This helps ascertain when assets are predicted to fail and can be used to predict the expectedremaining useful life of assets. 


This is useful information for energy companies for saving time (replacing or fixing those assets predicted to fail before they are completely broken and have to re-ordered/replaced etc.), saving costs (allowing companies to optimise maintenance activities based on accurate predictions), and reducing customer dissatisfaction (fewer unexpected asset failures should result in more consistent running and provision of power). 



Efficient Energy Storage  

Besides being beneficial for traditional coal and gas energy companies, renewable energy companies can too reap the benefits of Machine Learning tools. A commonly known issue with renewable energy is its sporadic availability. When there is no wind or sunshine for a while and therefore not enough energy production or, on the other hand, too much energy produced for the amount of consumption (causing the surplus energy to go to waste), it is essential for energy companies to optimise energy usage and storage.  

Machine Learning solutions can be used to predict energy usage and manage storage decisions. For example, ML models can manage electricity shortages by briefly cutting off the supply for electricity coming from the main grid, while using stores of energy from regions. This reduces unused and wasted energy supplies. 

In the case of saving and storing energy, Machine Learning can generate forecasts for weather, energy production, and electricity demand to optimise energy usages on “overly productive energy days” and thus reducing the need for cutting off main grid supply.  


The Future of Machine Learning and Artificial Intelligence in the Energy Industry 

For developed countries with “green” goals for an environmentally-friendly economy with resistant and consistent power supply is of top priority, Machine Learning algorithms that predict demand, improve performance, reduce costs, and prevent system failures are a clear step in the right direction.  


With the right partner, your energy company can reap the benefits of Machine Learning and get accurate, insightful, and actionable information out of your data to more accurately forecast energy demand, predict asset maintenance, and more efficiently store energySave resources, time, and costs – contact Intellify to hear how we have worked within the energy industry to do just that. We can help you achieve your Machine Learning and Artificial Intelligence requirements.