search.noResults

search.searching

dataCollection.invalidEmail
note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
ENERGY MANAGEMENT AND SUSTAINABILITY FUTURE FOCUS


Extracting value from the data generated within and by buildings is undergoing a radical change. Here, Cian Duggan, Chief Technology Officer at Carbon Credentials, explores how data collection management is evolving with AI.


A decade ago, most buildings in the UK had a couple of data feeds – usually electricity meters, or occasionally gas or water meters. Only larger buildings had meters which measured energy consumption every half hour, while the vast majority of meters were read monthly, quarterly or annually. Now, companies such as Carbon Credentials can analyse nearly 20m rows of data over 1.5bn data points at 15-minute intervals for over 40,000 buildings worldwide.


When legislation like the Carbon Reduction Commitment Energy Efficiency Scheme (CRC) arrived after the Climate Change Act of 2008, one of the key areas the scheme hoped to see improvement was the accessibility and availability of granular data from buildings. Indeed, in the first iteration of the CRC, nearly 2,000 participating companies were rewarded with higher place rankings in the first league table for ‘early action metrics’ including installing voluntary Automatic Meter Readings (AMRs).


“Modern architected systems take automated feeds in real time from thousands of sources globally to automatically check for patterns and anomalies.”


Having consistent and granular data streams (electricity and gas) was seen as an essential foundation for engineers and energy professionals to gain insight into consumption patterns. It also enables good business cases for energy conservation measures, and real measurement and verification of the savings of any investments in energy efficiency.


The first phase of European Energy Savings Opportunities Scheme (ESOS), completed in 2015 also required significant amounts of building data to be collected – covering nearly 8,500 participating companies in the UK. One of the key challenges with both schemes was the collection, verification and submission of building data.


As a leading auditor of CRC participants in the UK (having conducted over 350 mandatory audits of CRC participant’s annual reports) and completed over 300 ESOS audits in 2015, we saw first-hand the challenges in gathering data of sufficient quality, timeliness and completeness. Facilities management companies were often at the sharp end of this process, with the tenants and landlords of the properties reliant on the FMs’ data gathering systems and processes.


24 | TOMORROW’S FM


Fast forward to today and it is a changing landscape. With data collection at the meter source now becoming more available, the spotlight has turned to gathering data automatically including its completeness and quality, and sending alerts if errors or anomalies are found.


This is where algorithms, machine learning and artificial intelligence come to the fore. Long gone are the days where bill and data analysts pore over data feeds, manually entered basic databases or waiting for annual or quarterly summaries to determine possible actions.


Modern architected systems, like our ADAPt (Assured Data Analytics Platform) take automated feeds in real time from thousands of sources globally to automatically check for patterns and anomalies as well as regular data feeds from smart meters.


Smart buildings We’re now in a world of Smart Buildings and the Internet of Things. As well as data from consumption meters, bigger buildings for many decades have had sensors and controllers usually feeding back to a centralised Building Management System. These BMS have traditionally been difficult to extract data from.


But in a world of Smart Devices and AI, we’ve been able to successfully install many Secure Smart Building Gateways and interface with most BMSs in the UK. AI allows us to automatically scan the BMS network and pick up the most valuable points, and then automatically throttle back the frequency if required (many BMS underlying networks are not necessarily as robust as we might like – or of consistent quality across the whole building).


Good data collection management is essential to creating an accurate energy audit from thousands of data points to provide a complex model of how energy is being used, analysed against variables such as occupancy and outside temperatures.


Machine learning and AI come into their own to identify anomalies within how the building is operating for investigation and change. For example, we can quickly identify if sensors are providing errorful inputs, if pumps or valves are not operating correctly or if plant and equipment is running out of the expected range of settings. This leads to savings on average of 14% across all the buildings we support and critically helps organisations to reduce their carbon footprint.


IoT data The number of connected devices in the ‘Smart City’ sector is estimated to rise from 7.2bn in 2017 to 16.8bn by


twitter.com/TomorrowsFM


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74