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9 years ago
Use Analytics to Improve Operations and Energy Efficiency


Big data and predictive analytics are being used by business and industry to make changes and improve processes and operations in big ways. From handling 50 billion Facebook photos to decoding the human genome, the practical applications of big data are as diverse as they are exciting.

When it comes to facilities management and energy efficiency, big data is helping companies make changes that increase profits, improve operations, and reduce their carbon footprints.

Even with all the focus on financial growth, we often fail to recognize that growth can occur only if operations are efficient and harmonized with energy usage to reduce the total cost of operations.

Energy Metering and Monitoring

Since the 1880s, we have been metering the energy consumption of our buildings in much the same way. A century later, in the 1980s, submetering was introduced, mainly for the property management sector. For example, an owner of a shopping center could install submeters on each store and bill each one for its actual energy consumption.

Today, with the advent of big data, we are taking the concept of submetering to whole new levels. We are now using device-level submeters coupled with big data analytics as an engine for real time operational efficiency and system performance management.

As we transition from metering buildings to monitoring systems and devices, we can now measure more systems and operations, tie our financial goals to our energy goals, and gain visibility into all systems running an organization. Real-time energy data provides true insight into operations and, as a result, many opportunities avail themselves.

Opportunities Available through Energy Monitoring

When companies use systems that monitor the energy consumption of all their systems and devices, then use a big data engine to collect and analyze that information, the following happens:

Improved Operations. The combination of granular visibility into systems and devices, coupled with big-picture analytics and insights, enables companies to increase operational efficiencies and reduce costs. For example, device-level monitoring can identify incorrect scheduling of a building automation system on specific devices, like lights, in order to avoid energy consumption during off hours.

Advanced Failure Detection. When we monitor the energy-consumption patterns of devices and benchmark them against similar devices and locations, we get real-time maintenance alerts that enable us to avoid unexpected failures of devices, equipment, and systems. Identifying unseen problems and predicting failures before they happen saves thousands of dollars in wasted energy, equipment failures, and maintenance costs.

Interrelated Systems Performance. Before big data, we would not be able to discover systematically malfunctions that affect multiple systems. For example, if our device-level monitoring shows us that a water pump has malfunctioned, and, at the same time, we notice that an air conditioning unit has an energy waste peak, we can tie these problems together and solve them more efficiently.

Monitoring the Energy Print. Energy efficiency big data solutions enable a new solution that is a way to monitor building systems, motors, and other electrical equipment in real time. This is to monitor the energy print. When we analyze the energy print, predictive maintenance is possible. For example, when machinery begins to draw more and more electrical power, perhaps a belt or bearing is causing this increasing draw. With such energy trend information available, management can predict a compressor failure in advance. When we are able to utilize predictive maintenance, we save the time and money required for unnecessary maintenance.

Energy Optimization and Cost Savings. Using device-level monitoring, we can reduce energy waste by identifying inefficiencies in real time. Devices that are taxing your energy bill can be pinpointed and corrected.

Drive and Monitor Behavioral Changes. When we can explain the need for behavioral change, backed by granular energy data for every device and system in real time, describe the positive effect that employees’ actions can have, and celebrate their successes, we can create real long-term behavioral change.

Making the Move from Metered to Monitored

While making the move from metering our buildings to monitoring our devices is certainly beneficial, it is a process that should be conducted nonchalantly. Make sure to get buy-in for the project from everyone involved, from the C-suite to the people operating the equipment.

Like any company-wide project, this one should conform to whatever project and process technique your company backs. For example, if you use Six Sigma and DMAIC, follow the Define-Measure-Analyze-Improve-Control steps when creating your device-level monitoring project.

Throughout the project, make sure all stakeholders get reports of successes, as well as any failures and lessons. When you readily share the data you now have access to, everyone involved will benefit.

Whether it is for operational improvements, energy efficiency, responsible environmental stewardship, or a combination of all of these reasons, successful energy management is a goal shared by all companies. To accomplish this goal, we must be able to monitor it, benchmark it, report on it, and prioritize it. This is made possible by big data and the new innovations enabled by device-level monitoring.

Yaniv Vardi, Chief Executive Officer at Panoramic Power. Yaniv is a seasoned executive with close to two decades of executive leadership experience in the Enterprise Solution Industry. As CEO of Panoramic Power, he oversees the day-to-day operations of the company as well as provides vision, strategic direction and focused execution for the company. Connect with Yaniv on LinkedIn.

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