COLUMN – Automated diagnostics & analytics
October 3, 2014 - While looking for a theme for our October issue and we polled the industry about articles featuring occupant productivity with the fact that our industry has an amazing interaction with productivity within a building. Although all agreed that this was very true and a very powerful relationship, the general feeling was we need to keep working on how to shape occupant productivity into a real measured variable for our industry before we can include it in our ROI payback calculation. It was also noted that we need to educate the “C” suite about the important connections between our industry and employee productivity.
October 3, 2014 By Ken Sinclair
October’s theme then arrived at the door, with a knock, and the delivery of a very large book called Automated Diagnostics & Analytics for Buildings that Barney L Capehart and Michael R Brambley had asked me to write a foreword for.
Pushing the Envelope – by our contributing editor Jim Sinopoli, PE
Building Analytics beyond HVAC
Another contributing editor Toby Considine column makes reference to his chapter in the book as well, Autonomous Systems and Cloud Diagnostics.
Jim Lee’s article Predictive Maintenance came with this request to give the same book a mention as he had also written a chapter he wished to share.
A quick check of the table of contents indicated the collaboration of several other AutomatedBuildings.com authors. A review of the other new articles for October confirmed that this was a good theme. Plus how better to start building bridges to the “C” suite than with Automated Diagnostics & Analytics and the dream that in the future this will include the analytics of the measured variable of people productivity which is likely to somehow fall out of social media analytics.
Automated Diagnostics & Analytics for Buildings
It is a very large book, some 615 pages, the size of a small laptop but much heavier.
This book contains an amazing collaboration of the “who is who” of our industry and I was extremely pleased to be requested to write the Foreword. I am also very pleased that our very busy AutomatedBuildings.com contributing editors and faithful writers were able to find time to pen several chapters.
I am most impressed with the organization of the book placing the complex subject matter of the components of Automated Diagnostics & Analytics for Buildings in a organized manner in some 46 Chapters.
The printed graphics are also great in most chapters, helping immensely to depict the evolution of Automated Diagnostics & Analytics.
I am pleased to share my foreword with you as it provides insight and introduction to the book.
This book will help you explore the new world of Automated Diagnostics and Analytics for Buildings and provide insight and connection into the industry thought leaders that are taking big data into a new reality. “Dynamic Data Fuels Deep Analytics” speaks to the importance of the next level of deep analytics of almost everything will have and how we as an industry will provide a new level of deeper analytics connecting inquiring minds to almost everything with low cost real time data. The journey will be driven by the first wave of online analytics that will point to the potential of looking further into building operation opportunities, but further analytics will be required to factually quantify these opportunities. We all know analytics begat analytics.
Over the recent past, the best use of an analytic software application for building systems has been fault detection and diagnostics (FDD). FDD techniques are typically equipment or device centric and characterized by pre-defined rules based on an engineering model of a piece of equipment. Despite the impressive progress with FDD, the industry is in its infancy of utilizing data analytic applications in buildings. If analytics for the HVAC system has provided outstanding outcomes, we need to take that template to other building systems.
Several of the chapter authors are regular contributors to our free online magazine so understanding their thoughts and coming to know them in the following chapters will bring this book alive and make it relevant for many years to come. Once you know the industry thought leaders assembled in this book you can start following them and their most recent evolving thoughts in our and other online resources, their blogs and industry news feeds. The transition in the last few years has been amazingly rapid. In our magazine’s 15 year history we have talked about the possible but it is only in the last few years and even more accelerated in the last few months that the possible has transitioned into the plausible and our new reality.
Bring Your Own Device (BYOD) Mobility coupled with the cloud has created an industry of large building automation folks trying to rapidly understand the big data transition. Cloud based Big Data Projects are truly morphing into a dynamic collection of people, things, and internet interactions; a collaborator, not just a project. A “collaboratory” is more than an elaborate collection of information and communications technologies; it is a new networked organizational form that also includes social processes; collaboration techniques; formal and informal communication; and agreement on norms, principles, values, and rules” (Cogburn, 2003, p. 86). You will see in most articles that Ownership of the Collaboratory is an important piece of the total success of Automated Diagnostics and Analytics for Buildings.
A clear component of every successful energy integration Diagnostics and Analytics project is a team of champions who asserted ownership of the project collaboratory. The importance of keeping our data free inside the collaboratory needs to be highlighted; a lesson we learned in the past but somehow need to keep relearning. The data not only needs to be free, it needs to be named and organized in a predicable agreed on format.
It is not just the naming of data but a consistent data model that allows us to free our data to a world of dynamic dimensions for our own purposes. No longer must data be predefined before use if an accurate self-discoverable model is present. This new way of viewing data allows us a new world in which data can be used in several different ways as a dynamic subset of many scenarios.
I am very pleased that Barney and Mike asked me to provide my thoughts in this foreword for their new book. They have done an amazing job of capturing and assembling the new evolving frontier of Automated Diagnostics and Analytics for Buildings now occurring as part of the Internet of Everything (IOE).
I am extremely pleased that our magazine is a gathering and staging ground for paper publications and historical books such as this. There have been many other books in the past see our education page for linkage to them.
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