AutomatedBuildings.com Review - Defining the changes driving the self learning edge revolution

Several changes are driving the self learning edge revolution, here are links to a few;

This article explores the significance and evolution of IoT edge analytics. Since the author believes that hardware capabilities will converge for large vendors, IoT analytics will be the key differentiator.

IoT Edge Analytics is typically applicable for Oil Rigs, Mines and Factories which operate in low bandwidth, low latency environments. Edge Analytics could apply not just to sensor data but also to richer forms of data such as Video analytics. IoT datasets are massive. A typical Formula One car carries 150-300 sensors. An airlines for example, the current Airbus A350 model has close to 6,000 sensors and generates 2.5 Tb of data per day,. A city (for example the Smart city of Santander in Spain) includes a network comprising more than 25,000 sensors. To avoid these sensors from constantly pinging the Cloud, we need some form of interim processing. Hence, the need for Edge processing in IoT analytics. We can consider Edge devices from two perspectives: Evolution of the traditional Gateway vendors and Evolution of the traditional server vendors.

PMML becomes important for the ability to deploy models in multiple locations: Predictive Model Markup Language (PMML) PMML is an XML-based predictive model interchange format. PMML provides a way for analytic applications to describe and exchange predictive models produced by data mining and machine learning algorithms. It supports common models such as logistic regression and feedforward neural networks. (Wikipedia)

The 10 Algorithms Machine Learning Engineers Need to Know
Machine learning algorithms can be divided into 3 broad categories — supervised learning, unsupervised learning, and reinforcement learning.Supervised learning is useful in cases where a property (label) is available for a certain dataset (training set), but is missing and needs to be predicted for other instances. Unsupervised learning is useful in cases where the challenge is to discover implicit relationships in a given unlabeled dataset (items are not pre-assigned). Reinforcement learning falls between these 2 extremes — there is some form of feedback available for each predictive step or action, but no precise label or error message. Since this is an intro class, I didn’t learn about reinforcement learning, but I hope that 10 algorithms on supervised and unsupervised learning will be enough to keep you interested.

Data Science for Internet of Things (IoT) : Ten Differences From Traditional Data Science Data Science for IoT has similarities but also some significant differences. Here are 10 differences between Data Science for IoT and traditional Data Science. Working with the Hardware and the radio layers, Edge processing, Specific analytics models used in IoT verticals, Deep learning for IoT, Pre-processing for IoT, The role of Sensor fusion in IoT, Real Time processing and IoT, Privacy, Insurance, and Blockchain for IoT, AI: Machines teaching each other(cloud robotics), IoT and AI layer for the Enterprise

Will Machine Learning Reach Smart Buildings? This article had over 12000 reads last month and was republish in two other magazines.
Google, Amazon and Facebook are in the midst of reinventing themselves as Machine Learning (ML) companies. In fact, they are in heated battle to be ML’s #1 contender. They’ve recognized that ML is the edge that they need to be the best in advertising, cars, consumer marketplaces or whatever other business they’d like to enter in the future. ML algorithms have advantageous self-correcting behaviors that will be the best navigators of a digitized world. But, these come at the price of being more complex to understand and work with than, for example, rule-based analytics programs. And they require a continuous and ample supply of structured data to deliver any meaningful results. When Amazon Invests $35 Million Into Nest Competitor Ecobee, we can be sure that it wants access to contextual data from home owners to better compete with Google in ML. But how about all of the other software and hardware developers in the Smart Buildings, Smart Grid, and Smart City markets that are now touting their ML capabilities? Can they make the claims of an Amazon or Google?

Smart Commercial Buildings Show their Importance to The Internet Of Things In this new era our smart buildings have become a key aspect of the IoT, connecting all manner of objects and systems to optimise performance and enhance the way we live and work. - James McHale, Managing Director, Memoori

DC is the new power source in our buildings The shift to low voltage DC lighting will transform buildings we need to be part of that.

The Harmonization of the Smart Grid and Smart Building Markets has Begun! The Smart Grid and Smart buildings markets are beginning the tricky process of harmonization as major global firms look to capitalise on opportunities surrounding distributed energy. Allan McHale Director,Memoori Memoori sees the combined market for Energy Software in Smart Buildings (comprised of Enterprise Energy Management, BECS Supervisory Software and Smart Building to Smart Grid Interface Software) rising to nearly $10Bn by 2020, with related software on the Smart Grid side growing at a healthy 12% CAGR to nearly $2bn by 2020.

Cities--new and old, large and small--are looking for ways to differentiate themselves and provide stimulating environments for their constituents to generate economic wealth; be healthy and safe; learn and evolve; have cultural richness and sustainability; be productive; innovate; all while staying environmentally conscientious. Increasingly, we see that community leaders, planners, government officials, the business sector, and other municipal stakeholders are looking at the "smart city" movement to be a foundation for change and provide the platform for sustainable innovation and growth. Health-care, education, transportation, buildings, all become more intelligent and services-oriented as they are infused with leading technologies and innovations. The impact of networked services, applications, smart objects, and experiences is profound and will re-shape everything as we know it.

[an error occurred while processing this directive] Smart organizations understand that their primary goal is to enhance the performance of their people. In a knowledge work economy, people are the engine that keep companies growing, adapting, improving and innovating.

Not surprisingly, thoughtful workplace design can be a powerful tool for supporting employee performance. And a collateral benefit is that what typically supports productivity also enhances employee health and wellness – it’s all interrelated.

Given the nature of today’s work processes, how can the workplace better support human performance? How can the workplace support all of the different activities involved with knowledge work – things like collaboration, creativity, innovation, deep thinking and mentorship?

One way to better understand the nature of human performance is through a definition used by organizational psychologists, who believe it is enabled through a blend of ability, motivation and opportunity.

Wake-up call & Opportunity Industrial automation vendors have the unique opportunity to be on the right side of the automation technological advance, and to develop an open, multivendor interoperability ecosystem, but they must move quickly before the tipping point is reached and other companies capitalize on the significant changes dictated from outside the industry. The fundamental elements and standards already exist, if the industrial automation supplier community fully embraces and interoperable ecosystem and could potentially save their businesses from an early financial grave.

Connected Building Roadmap The intent of the session was to ask the attendees to look into the future, and tell BC Hydro what connected buildings would look like in ten years, and what technologies or other items of interest might appear in the meantime. Graham Henderson P.Eng. Sr. Program Manager, Commercial Marketing Energy, Regulatory & Business Planning BC Hydro

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