Each and every sector in the society relies on the energy industry for functioning appropriately. From agriculture to transportation and manufacturing, they consume energy every single day. The high demand for energy directly influences social development. That said, the utilities sector continuously faces challenges with smart energy management, environmental protection, and equal distribution. Furthermore, people face challenges regarding energy consumption and the application of renewable energy.
But, to our benefit, data science and AI can provide a solution to these problems and bring positive changes to the utilities industry. Here’s how.
Aiding The Transition To Renewable Energy
With the increasing energy demand and technological advancements, renewable energies are having a significant impact. More and more people are transitioning to renewable energy. However, a successful transition means something else for utilities providers.
To meet the demands of the public, the utilities sector has to run some numbers and analyze crucial predictive data. They need to analyze current renewable energy projects to understand when and where renewable energy is consumed in exceptionally high amounts.
This is where data science comes into play. With the help of data science services and predictive data, utilities providers can examine certain variables to cater to the needs of their customers. They must pursue the data to delve into the failures and successes of renewable energy projects to determine what steps they must take to invest in future opportunities to use renewables.
Outage Detection And Prevention
Power outages cause customers inconvenience and are extremely costly to fix. Regardless of the efforts made by utilities companies to optimize their services, power outages still take place. That said, to enhance customer satisfaction and improve the predictions regarding power outages, utilities companies must leverage the power of data and statistics.
Modern technologies and predictive data-driven power outage communication systems identify weak points in the electric grid to prevent outage before it occurs. It analyzes various factors/ conditions (like the weather, tree situations, etc) that can cause a power outage. These systems also predict the impact of electric lapses on the power grid. Therefore, helping utilities companies provide accurate real-time outage stats to their customers.
Identification of the root cause is the first step towards outage detection and prevention. And data science and AI can help utilities providers with the same.
Reducing Waste
For utilities providers, profitability and waste management are two factors that are intertwined. If they can’t reduce waste production, it might cost them a lot of money and customers, for sure. Help from data science companies and analytics have proved to be very beneficial for utilities providers to identify areas of inefficiency and cut down on water/ electricity waste.
By using branded apps, providers can offer customers guidelines and tips on conserving water, gas, and electricity; track utility consumption in specific areas, use AI to determine and identify leaks, and ultimately, reduce costs.
Energy Theft Detection
Every utility provider in the world has dealt with energy thefts multiple times in their lives. Energy thefts cost a lot of money and push customers to change their providers. But smart grid security solutions are perfect to tackle energy theft situations. They track the user behavior continuously and notify the provider in case of suspicious matters. This allows the utilities company to identify the hacker’s intention and dismiss it then and there, improving operational efficiency and enhancing customer satisfaction.
Final Words
In this world of instant gratification, providers are trying their best to meet the needs of their customers, by offering high-quality services at affordable prices. The same applies to the utilities sector.
Modern problems require modern solutions. Therefore, utilities companies must seek help from a data science company and invest in machine learning algorithms, analytic models, and big data solutions to tackle problems, improve operations, manage resources and avoid potentially expensive mistakes.
