Gene Boniberger’s scientific contributions

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Publications (9)


NYC Tall Rudin Commercial Buildings Energy Savings 2015
  • Presentation
  • File available

July 2015

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20 Reads

John Gilbert

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Gene Boniberger

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Temperature control module increases energy efciency and reduces costs in buildings \

November 2013

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19 Reads

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Vaibhav Bhandari

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[...]

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Gene Boniberger

OVERVIEW Forty percent of the energy in Manhattan is used in high-rise ofce buildings and, of that energy, forty percent goes towards controlling the heating, ventilation, and air conditioning (HVAC) systems controlling in these buildings. The costs associated with this energy are high, particularly for heating buildings with steam in the winter. This technology describes a module which helps forecast any necessary control adjustments that will keep the building at the desired temperature, and recommends preheating settings to reduce steam usage during peak hours. This technology not only increases energy efciency, but also reduces energy costs. Temperature trajectory module offers suggestions for HVAC temperature settings that improve energy efciency and lower steam costs This technology is another step towards more efcient, cost-effective energy usage. The technology is a temperature trajectory module that uses advanced analytics to make suggestions to the building manager who controls the oor-by-oor adjustments that modify the temperature. It uses the ambient space temperature, the current temperatures on each oor, and the various HVAC settings to make its recommendations for the upcoming two hours. Similar algorithms are used to make 24-hour predictions, which can determine whether to preheat the building with steam before peak hours render the steam a hundred times more expensive. This technology has the potential to provide great savings in energy and cost to Manhattan. A prototype module has been developed and tested in a tenant space over one winter season, reducing energy consumption by 7%, resulting in a savings of at least $75,000.




ALL 16 RUDIN BUILDINGS ELECTRICITY USAGE IN KWHR Winter Spring 2012-13

January 2013

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8 Reads

Preheat before start up (Hydro-Battery) = 175,000Morecontrolledstartup=175,000 • More controlled start up = 100,000 • Steering out of warm-up/cool-down = 50,000Continuousrecommissioning=50,000 • Continuous recommissioning = 72,000 • Ramp-down = 50,000Winteroverheating=50,000 • Winter overheating = 23,000 • Tenant baseload reduction = 35,000TotalSavingsover6monthsofwinter/spring,201213=35,000 Total Savings over 6 months of winter/spring, 2012-13 = 505,000 Savings



IBCON Case Study Poster 345 Park 560 Lex

January 2013

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123 Reads

With today's energy prices and the focus on conservation and sustainability, intelligent building operators need systems that provide reliable, real-time data that forecast effective system-wide decisions. At 345 Park Avenue, a two million SF skyscraper built in the 1960's and 560 Lexington, a 330,000 SF building with more modern control systems, we have developed the prototype for the building operating system required to simultaneously optimize all subsystems in large office buildings. These two buildings demonstrate a new operating system that analyzes continuous streams of data, responds with proactive operational recommendations, and learns the personality of each building, utilizing a Smart System that learns and improves with time. Goals: Operations Energy Efficiency Sustainability Tenant Experience Financial Optimization Challenges: • Prove that Energy Efficiency gains from manual control can be replicated by automated building operating system • Develop different classes of office buildings so that the installation and use are easy and quick • Training people in optimal building management • Communications integrated seamlessly with Intelligent Building Operations • Valid in International skyscrapers in different climate zones Successes: • We will demonstrate intelligent problem identification and solution recommendations that allow for continuous, real-time re-commissioning • Machine Learning provides a reliable forecast 24 hours in advance to recommend operating schedules that optimize tenant comfort and safety while minimizing energy consumption • Machine Learning also provides a now-cast of the next 2 hours to guide operators in set-points, preheating and cooling, and start-up and ramp-down timing • Tenants are engaged in the energy savings and environmental footprint. « « Sponsored By:


CASESTUDY Project Details Di-BOSS Digital Building Operating System Di-BOSS Digital Building Operating System Rudin Management

January 2013

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32 Reads

Di-BOSS is the next-generation Digital Building Operating System Solution that optimizes energy consumption, saving money and increasing security without sacrificing comfort. Di-BOSS continuously commissions data from multiple operations systems, floor-level occupancy, and ambient and forecast weather conditions. Data streams are integrated into a portable dashboard control panel, providing complete visibility.


Temperature control module increases energy efficiency and reduces costs in Smart Buildings

Forty percent of the energy in Manhattan NY is used in high-rise office buildings and, of that energy, forty percent goes towards controlling the heating, ventilation, and air conditioning (HVAC) systems controlling energy consumption in these buildings. The costs associated with this energy are high, particularly for heating buildings with steam in the winter. This technology describes a module which helps forecast any necessary control adjustments that will keep the building at the desired temperature, and recommends preheating settings to reduce steam usage during peak hours. This technology not only increases energy efficiency, but also reduces energy costs. The technology is a temperature trajectory module that uses advanced Machine Learning analytics to make suggestions to the building management system that controls the door-by-door adjustments that modify the space temperature. Similar algorithms are used to make 24-hour predictions, which can determine whether to preheat the building with steam before peak hours render the steam a hundred times more expensive. This technology has the potential to provide great savings in energy and cost to urban cities.