Smart cities’ infrastructure: analytics, interconnectivity and integration

by Ted Fagenson, EcoFactor

Using “smart” as an adjective is becoming a way to describe new categories of products and services. Smart phones outsold feature phones, but only after years of iterations and integration. Mobile Internet access was akin to dial-up speeds, and simple browser access was exceedingly frustrating. In the long run, it was the applications that drove the market forward, not just basic access. Once the Blackberry (or even the Treo) integrated email, the smart phone market became part of people’s daily business life. They became more productive. When the iPhone supported an expansive community of developers who provided innovative applications, the market was poised to accelerate. Widespread smartphone market acceptance arrived once Google’s Android platform unleashed a burgeoning original design manufacturer (ODM) market. “Smart” became useful, productive, innovative and convenient. It’s not just the connectivity to the Internet that matters; it’s the applications that were integrated into a single device. The same will be true of the smart home and smart city.

Smart home solutions today are essentially connected sensors such as door locks, lights, power plugs and video cameras that enable consumers to manually setup and program these sensors. Smart thermostats differ from their aging cousin in that they include connectivity to the Internet where one can control and maintain personal comfort levels. Similar to the feature phone market segment, however, these products should not be categorized as smart, but connected and available. So, how does the smart city market evolve from connected to smart?

Design and construction costs to engineer and build a city or a neighborhood that optimizes its homeowners to waste less time in traffic, take less time finding a parking spot and many other efficient lifestyle chores is enormously expensive. Inhabitants of existing cities must be able to cost effectively evolve into smart without the need to build out entirely new developments.

Efficiency begins with data interconnectivity. Basic data collection with real-time access is the foundation of a smart city. Sensor technology is becoming ubiquitous because of low manufacturing costs for products such as RFID modules and digital cameras. The plethora of wireless communications, including 4G, WiFi, Bluetooth and others, have allowed manufacturers to merge these technologies with most any sensor, spawning the phrase “the Internet of things.” or IOT. From motion and light sensors to temperature and humidity sensors to oxygen and pressure sensors, these components collect a lot of data that individually provides little value. Collectively, however, if organized and correlated to optimize around efficiency, data scientists can use the data to devise innovative algorithms previously unavailable. When supplemented with exogenous dynamic data such as weather, traffic patterns and work schedules, a data model that forecasts and maximizes efficiency quickly becomes complex. Terabytes of data are collected every few hours and real-time processing is required to provide valuable information to households and utilities alike.

Algorithms and models are great for optimizing and predicting around a set of variables, but value is derived from making the data useful and productive to consumers. It’s not enough to inform consumers how best to maximize their time or to minimize their energy usage or waste. The killer app in the smart city market segment is automation. It is the essential ingredient that enables adoption.

For instance, a connected thermostat provides information to consumers about their high-voltage, alternating current (HVAC) energy use and enables control from a remote location (consider the couch even though in many instances it is steps away from the physical thermostat). Other than control, however, most consumers will not spend the time or effort to minimize the time that the HVAC operates. It’s just too time consuming. Furthermore, as the cost of electricity shifts from a flat rate pricing structure to a dynamic pricing policy that better reflects the cost of generation, the complexity for a consumer to optimize around this is too taxing. Instead, an intelligent algorithm will incorporate these factors and provide a mechanism to automatically adjust settings for the consumer based on comfort levels and cost of electricity.

Analytics resulting in automation extends beyond just the thermostat. Imagine if electric grid operators could better forecast electricity consumption. If they knew that the fleet of Teslas in a particular neighborhood were scheduled to fill their tanks, automatically set to charge from 1:30 a.m. to 4:30 a.m. Electricity providers would know the total consumption per automobile as well as the rate of charge.

With solar cells becoming more cost effective, distributed generation’s effect on the grid contributes another set of data to the smart city predictive model. With natural gas prices forecasted to remain low due to the large volume of reserves, it is believable that local electricity generation from products such as Bloom Energy may completely disrupt the flow of electrons throughout a smart city. Electron flows are no longer as simple as one-way into a home or business. Once local generation becomes more pervasive, electricity pricing is likely to adopt a dynamic pricing policy. For cities and consumers to adapt to this new system, pricing becomes a data element to incorporate into the demand model to automate when electricity should be consumed and when to avoid expensive times. Never having to think about the cost of energy, but rather have it optimized automatically is the real killer app. Integrating a wide array of sensors with consumer habits and lifestyle changes into a sophisticated real-time model enables smart city inhabitants to gain significant cost efficiencies effortlessly.

Ted Fagenson is EcoFactor’s chief marketing officer, responsible for marketing strategy and communications, channel marketing and product management. He was previously COO and vice president of sales at CarCharging, an electric vehicle charging service that serves the SaaS infrastructure marketplace. Prior to joining ChargePoint, Fagenson was vice president of corporate marketing and sales for Cellfire, a mobile promotions service. He received an MBA from the University of Rochester Simon School and a bachelor’s degree in electrical engineering from Rutgers University.

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