CEO Mark DeSantis to moderate Big Data for energy management panel at BuildingsNY

Thursday, 5/3/2012 1:30 pm – 2:30 pm

Big Buildings. Big Data

 

Topics

The panel will discuss advances in ‘Big Data Analytics’ or real-time data analysis and how it is revolutionizing energy management tools and practices.

 

Description

 

Traditional hardware and software solutions in energy management suffer from the same limitations that all standard data warehousing tools suffer from: large and slow production of information of very limited value for real-time decision-making. The ‘information’ that is often generated by most current energy management systems is of limited or no practical use to those directly responsible for managing energy day-to-day because it draws from a limited number of static data sources and is analyzed post hoc. Energy managers know this and are looking for quick access to actionable information culled from multiple data sources in real-time. Big Data tools are already well established in social networking and online commerce and an even larger opportunity is now available in energy management software in areas as diverse as real-time load management, power quality improvement and the buying and selling of power. Harnessing the power of multiple data sources, as in the phenomenon of the Internet of Things, will be about technologies that go well beyond traditional data warehousing. It will require processes that imitate the way the human brain functions. Our brains take in massive streams of sensory data and make the necessary correlations that allow us to know where we are, what we’re doing, and ultimately what we're thinking.

 

Learning Objectives:

1. Understand what the ‘Big Data’ phenomenon means for large consumers (>1MW) such as the extraordinary energy and sustainability possibilities inherent in different types, sources and uses of existing electricity, steam and gas data. 

 

2. Learn why smart meters are not (yet) very smart and what you can do

about it.

 

3. Learn how, what, and where the data you already generate (and probably discard) can improve your energy efficiency, your power quality and your energy procurement.

 

4. What does this mean for me cost/benefit-wise.

 

 

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