If your organization isn’t currently leveraging data science, you’re probably behind the curve. Businesses across industries are looking for ways to implement data science and machine learning to improve and automate their core processes—and the energy industry is no exception.
In fact, renewable energy companies (wind, solar, hydro, and nuclear) have greatly benefited from the power of data science, machine learning, and AI over the years. They’ve managed to lower their costs, make better predictions, and increase their portfolio’s rate of return. And, this trend is only going to continue at a more rapid pace.
If your company operates in the energy sector—or consumes massive amounts of electricity—chances are that data science can help boost your business performance. But how exactly? Let’s dive into it.
Five Ways Data Science Is Changing Energy
There are so many ways that AI can be used to positively transform the energy sector. Here are just a few of the most popular applications in the works today.
1. Grid Management
One of the most interesting uses of data science 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.
Although there are numerous ways to store energy, the most common way is the archaic but still efficient method of pumped hydroelectric storage. It works by pumping water to a certain elevation and then harnessing it again by allowing it to fall onto turbines.
When dealing with renewable energy, it’s difficult to predict the grid’s electricity production capacity—after all, it depends on several factors such as sunlight and wind. Using data science capabilities, organizations can better forecast and understand how to maintain equilibrium within their power grid.
2. Predictive Maintenance
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.
In 2003, low-hanging high voltage power line brushing against an overgrown tree caused a massive blackout in Ohio. The power system alarm failed, and there was no indication that the incident had occurred. The electric company did not discover anything until three more power lines started failing for similar reasons. Ultimately, this oversight caused a cascade effect, resulting in the entire grid going down.
The blackout lasted for two days and affected 50 million people. Additionally, 11 people died and there were about $6 billion in losses incurred.
Where AI and Internet of Things (IoT) come 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. The main purpose of these algorithms is to efficiently predict machine failure, avoid blackouts or downtimes, and optimize maintenance activities and periodicity, thus cutting down on maintenance costs.
In the United Stated, for example, they started installing phasor measurement units to prevent failures in the power lines. They can track:
- Voltage & current
- Location (through GPS)
- Timestamp (in microseconds)
- Device ID
Events like the Ohio blackout can now be averted completely. Ultimately, AI and ML can help energy companies switch from a reactive maintenance stance to a proactive, predictive maintenance stance.
3. Demand Forecasting
To ensure operations run smoothly, businesses in the energy sector also need to guarantee that their customers are receiving the best service possible. Regardless of industry, call center support demand is always variable. However, it’s especially unpredictable in the energy industry.
When large swings in demand occur, it puts an immense strain on call centers. One of RapidMiner’s customers, FirstEnergy, receives 16 million calls for their 700 call center employees every year—they needed a streamlined forecasting system to understand all volume drivers and make accurate predictions.
Solving demand forecasting issues
FirstEnergy decided to use data science to forecast call volume using all volume drivers—the weather forecast, historical data, etc. Though they had an old forecasting system they were using in the past, it was very manual and relied heavily on short-term correlation.
This new data science-powered solution was fully automated and significantly more accurate (it achieved an average 93% accuracy for the 90-day forecast), with thorough documentation and recognition of knowledge gaps. On average, these forecasting models saved each call center $665k annually.
4. Energy Source Exploration
Besides demand forecasting and optimizing resources, AI is being used in fossil fuel energy source exploration and drilling.
Two years ago, Exxon Mobil teamed up with MIT to produce self-learning submersible robots to explore the ocean surface. These robots will be equipped with machine learning algorithms to not only help them learn from their mistakes while conducting explorations, but also to carry out the same work that a scientist would, without the risk.
The robot will explore, record data about the ocean floor, and make an analysis based on the data to help identify new locations to drill oil and natural gas.
5. Energy Consumption
Switching to renewable energy sources isn’t just for governments and electric companies to focus on. In fact, numerous companies like Google and Microsoft have tried to make an impact on the environment and their bottom lines by lowering their overall energy consumption.
Google is well-known for its massive data centers established around the world. These data centers produce a great deal of heat, which require a massive amount of electricity to cool down.
To address the problem, DeepMind AI used machine learning algorithms to reduce energy cooling on its Google data centers by 40%. Aside from greatly reducing their utility bills, it also helped to lower overall emissions, reducing the carbon tax they would otherwise have to pay.
The Future of Data Science in Energy
Machine learning and AI certainly have a long way to go in the energy sector. With developed countries aiming for a completely green economy, maintaining a balanced, resilient, and reliable power grid is a top priority.
That’s where smart grids come into play. Smart grids are power grids which combine the power of IoT and AI to create a digital power grid that enables a two-way communication between consumers and utility companies.
Smart grids are equipped with smart meters, sensors, and alerting devices which continuously gather and display data to consumers so they can improve their energy consumption behaviors. It can also be fed to machine learning algorithms to predict demand, improve performance, reduce costs, and prevent system failures.
Although smart grids are being adopted in several developed countries, we still have a long way to go before switching to 100% renewable energy sources, AI-controlled power distribution, and grid management.
Choosing a Data Science Platform for Your Energy Business
The emergence of AI and machine learning have demonstrated the myriad of ways data science can impact organizations across industries—from forecasting demand to optimizing campaigns to streamlining supply chain operations.
However, you can’t act on emerging technology initiatives without selecting the right data science platform for your team. There’s much to be said for partnering with the right experts to ensure you get the most accurate, insightful, and actionable information out of your data.
Want to learn more about what to look for in a data science vendor? Check out our DSML platform Buyer’s Guide for tips and tricks.