Editor’s note: Big data analytics has already taken root in the energy industry. In this article Alex Bekker, Head of Data Analytics Department at ScienceSoft, describes how exactly big data analytics can drive value in the electric power sector. In case you’re one of those considering the launch or improvement of a big data solution, you are welcome to explore ScienceSoft’s offering in big data services.
Electric utilities go for smart grids with advanced metering infrastructure and big data capabilities to get strategic insights that would foster efficient energy use. Based on the experience gained from our cooperation with electric utilities, I will show you three practical examples of how big data analytics augments the energy industry.
It’s not news that failures in the energy industry equipment may result in catastrophic power blackouts and vast sums of money spent on new assets, restoration works and energy losses. To avoid or minimize such outcomes, I advise developing an efficient equipment monitoring and predictive maintenance approach, the key technologies of which are smart meters and big data. As well as sensors, whose operating principle is described by my colleague, Alex Grizhnevich, in the article dedicated to IoT-based predictive maintenance, smart meters generate all kinds of equipment state data to communicate disturbances, their localization and fault types to the utility in real time. It allows electric utilities to employ advanced big data technologies to detect disturbances early enough to avoid breakdowns and costly downtimes.
Electric power quality influences the safe operation of a power grid and consumers’ satisfaction. Fortunately, big data software goes far beyond detecting disturbances a posteriori. For example, we at ScienceSoft offer our customers to implement continuous power quality monitoring to create “an early warning system” empowered with deep learning and pattern recognition algorithms. With this system, you can analyze all the information related to power quality, detect and classify deviations from the norm appearing in power grids quickly and accurately. Once the deviation is classified, it is possible to determine its causes and take measures to prevent it, avoiding downtimes and production losses.
Advanced big data analytics methods enable accurate load forecasting, which is the basis for effective energy management. Here, let me set an example of how data science can help electric utilities forecast the load and save the investments. To enable that, they partition the geographical area according to local weather information and use data from smart meters. Smart meters, constantly collecting data, feed the AI technologies with data to identify consumers’ typical behavior, match behavior patterns with historical data on weather conditions and make accurate predictions about customers’ real-time behavior under certain weather conditions. And utilities are not the only ones to win in this situation: in combination with in-home displays and programmable communicating thermostats, electric power users obtain the information that can encourage them to initiate a change in the energy consumption, which advances the era of conscious energy consumption.
Considering the benefits that predictive maintenance, power quality monitoring and load management bring into the energy industry, many electric utilities have already leveraged big data analysis. The use cases I’ve outlined here are by no means exhaustive. If you’d like to learn more about the value, which big data analytics may bring, you are welcome to explore the overview prepared by my former colleague Olga Baturina.
A big data journey may be long and full of risks to overcome. But I’m convinced, with the right strategy at hand, the result is always worth it. If you are not sure where to start or think your current big data solution leaves something to be desired, ScienceSoft’s data analytics team will be glad to help, just let us know.
Big data is another step to your business success. We will help you to adopt an advanced approach to big data to unleash its full potential.