IBCon Internet of Things: Ten Years of Lessons Learned

© Siemens Industry, Inc. 2015 All rights reserved. http://siemens.com/rcs

The Internet of Things - Ten Years of Lessons Learned

http://siemens.com/rcs

© Siemens Industry, Inc. 2015 All rights reserved. Building Technologies Division

The Site Controls platform helps retail chains achieve energy efficiency and smart consumption goals

Seven thousand installed sites with premium clients: ✓ Retail (small, medium, & big box) ✓ Grocery ✓ Restaurants ✓ Health Clubs ✓ Branch Banks


Enterprise vs. "Building-Centric" approachSite Controls Enterprise Portal

On-demand exception management vs. alarm bombardment is one key

• Voluminous data isn't actionable; Information when prioritized is

• Identify most expensive enterprise problems first for best returns

• Verify corrective actions and service work remotely for all buildings

• Integrate & prioritize all energy consumption data into one platform

• "It's a Process not a Project”

Typical Customer: several hundred sites, several thousand HVAC units, several thousand lighting circuits, ten thousand sensors, a thousand energy meters


Big Data!

• A typical site generates about 40,000 timestamped data points per day.

• That’s 40 million data points per day for an enterprise of one thousand sites.

• In a year… it’s too many to think about

• Without good analytics, the data is overwhelming


Alerts and Alarms are not the answer

If only 1 in 10,000 data points causes an alert or alarm…

…and an enterprise generates 40 million data points per day:

4,000 Alerts or Alarms per day

“Alarm Fatigue”

http://en.wikipedia.org/wiki/Alarm_fatigue


All of these buildings are in the Real World™

It’s a messy, complicated, uncooperative, unrelenting place.

Bad Conditions: things get smashed, painted over, disconnected and relocated

Bad Comms:

sometimes no comms at all, sometimes garbled comms

Bad Devices: sensors fail, sensors become uncalibrated, devices age

Human “Help”: humans are smart in an evil way




The natural environment is

harsh…




…not just harsh, but dangerous!




Things wear out in the Real World™, often

when no one is looking.

Fan Belt




Humans are the worst!




Lamp placed so as to heat thermostat

Humans are the worst!


Plan for failure at every level

Controller/Gateway

Sensors/Devices

Wireless

Wired (Modbus)

Internet

Store

PrivateCorporateNetwork

VPN

Siemens Site

Controls

X

XX

X XX

XX

X - Point of failure


Lucky us! The Real World™ runs on physics!

• Real-world physics limits the possible correlations in data

Example: a rooftop HVAC unit’s performance will not have any effect on the performance of a boiler in the basement

• This means that rules are more effective than statistics for analytics

Example: Outside Light Sensor should see regular periods of brightness and darkness. If not, sensor is likely broken, damaged, painted over or otherwise out of order.

Image by By Brews O’Hare (Own work)via Wikimedia

In Real World™ analytics, rules are usually handier than statistics


Use data aggregation and ranking in your analytics toolkit

• Aggregation:

kWh / square foot / day Min kW, Max kW Average outside air temperature KPIs, eg, site connectivity

• Rules detect exceptions exceptions have scores

• Rank sites by aggregating exception scores in various categories:

HVAC Health Energy Use Site Connectivity On-site Comms Etc…


Aggregate exception scores to rank sites


All of these techniques used together lead us straight to the problems

HVAC not just broken, but blowing hot air


Nature is tough

Human beings are worse more devious

Alerts and Alarms won’t cut it with Big Data

Plan for failure at every point

Rules are better than statistics in the Real World™

Aggregation reduces the amount of data you need to digest

Ranking makes the best or worst easy to find

Lessons71

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http://siemens.com/[email protected]@robhafernik

IBCon Internet of Things: Ten Years of Lessons Learned

Data & Analytics