How can you leverage “big data” for energy savings?

This audio was created using Microsoft Azure Speech Services

Energy efficiency has been in the spotlight for decades, particularly in regard to buildings, which today account for 42% of the world’s energy use.  Facility managers rely on building management systems (BMS) to gather data about building performance and energy usage in their quest to reduce operating and maintenance costs, improve building comfort, and save energy.

But harnessing “big data” to leverage BMS potential requires significant training and in-depth knowledge of a facility and its history, along with an investment in IT and dashboards or automated analytics. Throw in an aging infrastructure, reduced budgets, and expertise lost through personnel turnover along with the demand for sustainable, high-performance buildings – and it’s obvious that facility managers face a terrific challenge.

So, what approach works for managing all that big data? Take a look at the following options:

Pro-Cons of Analytics Approaches
Trade-offs of different analytics approaches

The combination of data analytics, managed services, and ongoing support from experienced building engineers can have a real impact on energy consumption, operational efficiency, occupant comfort, and the financial well-being of buildings. The right Managed Software as a Service (MSaaS) analytics solution will proactively help facility managers achieve performance-based utility incentives and build a lower carbon footprint—all while driving a positive ROI, increasing portfolio value, and maximizing BMS investments.

A detailed guide is available in a white paper I recently authored, Optimizing Buildings Using Analytics and Engineering Expertise.

If you have questions or wish to discuss, please leave a comment in the box below.

Information flows from data to results; analytics reduce facility operating costs over time by targeting maintenance efforts
Information flows from data to results; analytics reduce facility operating costs over time by targeting maintenance efforts

 

Tags: , , , ,