What role does data science play in the energy sector?
The energy industry is the driving force behind the modern economy. Every industry relies on the energy industry to function without interruption.
Today, energy consumption has reached an all-time high. According to industry reports, Australian businesses use up to $20.2 billion on electricity every year; this is the equivalent of using 154,439 gigawatt-hours of electricity. Even small businesses may use up to 36,000 kilowatt-hours of electricity per year.
In response to this increasing demand for energy, the energy sector is looking to develop new methods to optimise electricity usage as well as find potential alternatives for energy generation, and big data analytics is playing a critical role in this process.
Here are a few examples of the roles data science plays in the energy sector.
Improving theft detection and smart grid security
With the growing need for energy, it’s no surprise that some individuals and even businesses may turn to illicit means to get electricity.
In fact, energy theft has become a significant issue for energy companies in recent years. Each year, energy companies lose an average of $89.3 billion to energy theft.
Today, energy companies are using data science to prevent energy theft. Many companies use advanced metering infrastructures that report energy usage, which allow them to observe the flows of energy and identify irregularities.
By keeping an eye on the behaviour of users and comparing these with previous instances of energy theft, energy companies can detect potential bad actors trying to steal from energy grids and take necessary measures to prevent them.
Balancing supply and demand
Balancing supply and demand is one of the keys to effective energy management.
When it comes to energy, both high and low demand can lead to many issues, including increased expenses for both consumers and energy companies.
Energy companies, therefore, need an efficient demand response strategy to find the perfect harmony between supply and demand and data analytics solutions can help in this process.
With real-time management solutions and applications, energy companies can monitor the metrics of energy usage and adjust the energy supply to meet the demand.
Improving outage prediction
Power outages are a common problem many businesses have to face.
Although power outages have become less common over the years, they can still happen due to several unexpected events and leave thousands of people without power and bring business operations to a halt.
For instance, the state of Texas in the USA suffered a major power outage recently due to inclement weather conditions that lasted for several days.
To tackle this, energy companies are now using data science and other forms of data analytics to enhance outage detection and prediction. Using these solutions, energy companies can gain insights on the effect of weather on power grids and potential outages in specific areas
Using this data, energy companies can predict outages by identifying the metrics and their threshold values and detect the cause of outages.
Once the causes have been identified, energy companies can take measures to keep their energy flow in check, and warn people of potential blackouts.
Improving customer experience
By the end of the day, energy companies are still companies, and they rely on their customers to turn a profit. For every energy company, the needs and requirements of its customers are a priority.
Energy providers can get valuable data on their customers regarding their behaviour and energy usage patterns, which can then be used to find meaningful relationships between power supply and customer demand and customise services and recommendations for their customers.
Modern data science has changed the energy sector forever
While data analytics have always been present in the energy sector, energy providers have come a long way from using static models and algorithms for data analytics.
With new real-time data analytics solutions, the data science applications for the energy sector are almost endless.
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