The concept of Smart Cities offers the promise of urban hubs leveraging connected technologies to become increasingly prosperous, safe, healthy, resilient, and clean. What may not be obvious in achieving these objectives is that many already-existing utility assets can serve as the foundation for a Smart City transition. The following is a broad discussion on the areas of overlap between utilities and smart cities, highlighting working knowledge from experience at PG&E.
The Heart and Soul of Smart Cities
Without doubt, human kind has made incredible technological strides in the last 100 years. Electricity, semiconductors, computing, the Internet, and near ubiquitous broadband communications are just a few of many examples. Most would agree that humans are much better off for our progress. However, as the proud father of an inquisitive twenty-month-old daughter, I cannot help but wonder whether her world will be better than mine is today. Some contend that our human prosperity will continue to grow as we head into the Second Machine Age, but I would temper this optimism by pointing out the price the planet is paying for this incredible progress. Climate change is real, plastic pollution is rampant, critical resources are depleting, there are growing social tensions across nations, and animal species are dying out daily amid the sixth, sadly man-made, mass extinction.
Fortunately, organizations around the globe—from ports and cities, to states, countries and entire regions—are starting to take action, often via aggressive climate action plans (CAP). Carbon is a key antagonist in the Earth’s environmental tale, which is why most CAPs call for massive reductions in carbon emissions and ultimately 100 percent use of clean renewable energy instead of carbon-creating sources.
Massive technological breakthroughs will be required to implement the requirements of these CAP plans, but we have already established the technical foundation for a smarter world – assembling the ground floor out of critical building blocks of infrastructure, sensors, edge processing, ubiquitous communications, data storage, analytics and control systems. We are also changing our perception about what it means to be smart. As we have seen in previous blogs in this series, brilliant smart solutions require ecosystems, partnerships, creative financing, and radical policymaking. Like natural systems, man-made smart systems—even apparently disparate systems—are connected and need holistic management. Silos are out; systems, plans, and think-tanks that share data are in.
The Second Machine Age is synonymous with Smart Planet Big Data and Big Math, which enables solutions to become more encompassing and optimal as they become more holistic in nature. For years, Moore’s Law has characterized the growth in computing capabilities, which has driven truly impressive developments in the last 30 years. Some predict that we are now at an inflection point, and tomorrow’s steps forward will be so staggering that machines will soon accomplish what until recently was thought of as science fiction.
The rise of the machines is due to cheap parallel computation creating new possibilities for neural networks, the primary architecture for Artificial Intelligence (AI) software. No longer centralized to discrete locations, AI applications run Cloud-wide on open standard servers that process hundreds of instances at once, all cleverly integrated into a unified stream of intelligence. AI applications are in the palms of our hands, literally, via a myriad of handheld devices, as well as network connected devices. With open access, AI is on track to become the next common utility, available to solve almost any problem.
Our smart progress also stems in large part to the development of algorithms that sift through masses of data and organize complex combinational relationships between hundreds of millions of neurons. Deep learning (also known as deep neural networks), is an emerging field of machine learning that could lead to new smart opportunities. Computer scientists feed these networks stacks of data, which the network “learns” and classifies on multiple layers at different levels of abstraction. In short, computers are teaching themselves. Deep Learning is advancing speech recognition (think smartphone functions and machine translation) as well as image recognition using only pixels to identify images (this is how Facebook automatically tags friends in your photos).
From this perspective, the Second Machine Age offers redemption as well as progress. Through deep learning, we can evolve way beyond self-driving cars and robotics, to a time when data and networks can accurately model the planet in real-time. Imagine this! On the grandest scale there is, we can forecast climate change, pollution, water shortage, and species risks to understand potential global impacts and mitigate detrimental outcomes. With this astounding insight, we can balance a prosperous humanity with planetary harmony.
While machine learning, data, and algorithms are the heart of smart city progress, humans are the soul. Politicians, bureaucrats, engineers, financiers, grey-haired technology veterans, and bright-eyed college graduates—the decisions we make at the beginning of this Second Machine Age, will define our smart landscape, shaping society and systems like water-carved canyons. Step up! Vigorously embrace radical, out-of-the-box thinking and do not fear missteps along the way. Apathy is the only real hazard as we strive for a smarter world. As Jennifer James said in the first blog of this series, “…this is the time for massive innovation and experimentation, favoring creativity over structure”, as we strive to solve some of the greatest challenges the planet has ever faced in the light of human prosperity.
For more insight into smart city trends, review Black & Veatch’s Strategic Directions Smart City/Smart Utility Report (2016).
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Spotlighting innovations in urban sustainability and connected technology
When the idea of smart cities was born, some ten to fifteen years ago, engineers, including me, saw it primarily as a control system problem with the goal of improving efficiency, specifically the sustainability of the city. Indeed, the source of much of the early technology was the process industry, which was a pioneer in applying intelligent control to chemical plants, oil refineries, and power stations. Such plants superficially resemble cities: spatial scales from meters to kilometers, temporal scales from seconds to days, similar scales of energy and material inputs, and thousands of sensing and control points.
So it seemed quite natural to extend such sophisticated control systems to the management of cities. The ability to collect vast amounts of data – even in those pre-smart phone days – about what goes on in cities and to apply analytics to past, present, and future states of the city seemed to offer significant opportunities for improving efficiency and resilience. Moreover, unlike tightly-integrated process plants, cities seemed to decompose naturally into relatively independent sub-systems: transportation, building management, water supply, electricity supply, waste management, and so forth. Smart meters for electricity, gas, and water were being installed. GPS devices were being imbedded in vehicles and mobile telephones. Building controls were gaining intelligence. Cities were a major source for Big Data. With all this information available, what could go wrong?
If you want a healthier community, you don’t just treat illness. You prevent it. And you don’t prevent it by telling people to quit smoking, eat right and exercise. You help them find jobs and places to live and engaging schools so they can pass all that good on, so they can build solid futures and healthy neighborhoods and communities filled with hope.