Data as a Critical Element in the Discovery and Delivery of Smart Energy Posted on : Apr 22 - 2017

By 2020 Gartner analysts estimate an astronomical number of 25 billion devices will be connected to the internet. Dubbed as the internet of things (IoT), a sizeable portion will be sensors. In the energy and utilities industry, these sensor devices will comprise of meters, gauges, and thermostats attached to energy turbines, oil drills, smart home appliances, solar panels, power stations, and windmills—all as part of a smart grid. They will act like incessant, inaudible chattering sentinels spewing electrical impulses as data streams.

In this savannah of raw data streams are invaluable insights. By using machine learning techniques to implement predictive statistical models, data scientists can discover valuable insights and illuminative patterns from these data streams. Utility companies can use these results to deliver efficiency energy usage, leading to smart decisions and cost savings.

Without using machine learning predictive models and real-time detection of defective devices, you cannot discover trends. Without discerning trends you cannot deliver energy savings.

The Future is here for Smart Grid

Yet you don’t have to wait until 2020. Today, utility companies are employing Apache Spark and MLlib algorithms to tap into sensor data to capitalize on savings. As well building predictive models, managing and optimizing assets, and monitoring the general health of the sensors and grid, data scientists at energy companies can predict potential faults and prevent failures in their electrical grids

Harnessing the power of machine learning

Thomas W. Dinsmore explains what machine learning (ML) can do for your business. By citing various use cases, he examines how ML algorithms can detect cancer pathology better than trained human reviewers; classify paintings into historical genres quicker than art curators; detect fraudulent credit card transactions faster than humans; and recognize voice and image rapidly than we do, across vast array of images and audio files.

However, these ML techniques can be extended to other sectors, argues Bruce Harpham in “How data science is changing the energy industry.” Harpham suggests that innovation and use of ML techniques borrowed from other sectors that Dinsmore above refers to can be transferred to the energy sector. He elaborates on how energy sector is already building smart saving grids.

For example, Francisco Sanchez, president of Houston Energy Data Science, says that “The energy industry has recently started to adopt the survival analysis concept from the medical field.” Used in medicine, the survival analysis is a statistical method to predict survival rates for patients based on their existing conditions and treatment program.

This model is now being applied to predict field equipment health in the oil and gas industry, Harpham writes, enabling early detection of defective equipment, preventing failures and outages, and improving reliability of energy consumption. View More