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Intelligent Building Management

References:

Buildings Need a Brain Book
Why and How Scaling down TPO's ML into a Residential building
New York City Wins, The City Wins!
Creation of a Columbia Machine Learning System to Optimize the Recharging of Electric Delivery Vehicles in large urban cities Proposal for: Creation of a Columbia Machine Learning System to Optimize the Recharging of Electric Delivery Vehicles in large urban cities
Smart Fedex Electric Fleet Depot Center for Lower Manhattan
MACHINE LEARNING TUTORIAL
DiBOSS Demo 2017
Elsag Columbia Rudin Press Release June 5 2013
Using an Ancillary Neural Network to Capture Weekends and Holidays in an Adjoint Neural Network Architecture for Intelligent Building Management
Columbia Campus Energy and Environmental Management System 2012
Visuals of Manhattan from Columbia Perspective 2018
Columbia MLS Bubble Plan 2012
Rudin Columbia Integrated Building Portfolio Management System 2012
Columbia NWC Technology Challenge 2012
Smart Property Mgment Biz Plan 2012
Smart High-Rises OPTIMIZING ENERGY PROPERTIES
Princeton Columbia Energy Management Poster for High Rise Buildings 2012
NYS CAIM Grant Application Total Property Optimizer for the Smart Property Management System 2013
Concept and Broad Requirements for the Grid Supply Stability Signal within TPO 2012
TPO Status end of 2012
Smart Building Business FNM Path to Smart Building Market
Building Thermal Response Modeling Final Project Report
Customer Risk Notification System 2012
Great Northeastern US Blackout of 2003 Advanced Warning Next Time23 Eastern Daylight Time
345 Park and 560 Lex Horizon Indicators Forecast vs Actual 2013
345 Park Heat Map by Floor Animation 2013
History Now Future BlackRock Launch Tenant 2013
Problems found by TPO Shift Engineers using the TPO next to the BMS for real-time commissioning of 345 Park and 560 Lexington
DiBOSS Interface Use Cases 2013
Comparison of Di-BOSS to Competitors 2013
Adaptive Stochastic Control for Load and Source Optimization of the Electric Grid 2013
FINSENY Smart Buildings : Use Cases Specification 2013
Columbia Improvements in TPO for DiBOSS
Error score analysis: 345 Park Steam demand
Error score analysis: 345 Park Electric predictions
Electricity Consumption RUDIN COMMERCIAL PROPERTIES 2012
Semiparametric Estimation of a Gaptime-Associated Hazard Function 2013
IBCON Digi Award Application 2013
ALL 16 RUDIN BUILDINGS ELECTRICITY USAGE IN KWHR Winter Spring 2012-13
Di-BOSS IBCON Sell Sheet Poster 2013
Columbia Engineering Machine Learning System 2013
DiBOSS Keynote IBCON 2013
CASESTUDY Project Details Di-BOSS Digital Building Operating System Di-BOSS Digital Building Operating System Rudin Management
IBCON Case Study Poster 345 Park 560 Lex
DiBoss Scrolling Convention Presentation 2013
DiBOSS Heat Storm 7-15-20-2013 345 Park Ave and 560 Lex
Now-Cast Module Writeup of TPO
TPO 24 hour Forecasting uses SVM
Di-BOSS Networking Systems Integration 2013
Training teams in how to analyze TPO and Di-BOSS Big Data
Analyzing Weather Observation History to Improve Building Energy Demand Predictions Analyzing Weather Observation History to Improve Building Energy Demand Predictions (Cont.) Quality Control of Data Conclusions and Points for Further Research
Report on Advantages of Ensemble Learning Model Based on Hidden Markov Model (HMM) for TPO v2
Columbia in Selex ES DiBOSS Evolution 2014-15
Research Report: Improvement in TPO Forecast Performance and Automation using Ensemble Methods and Unsupervised Learning
Analyzing Weather Observation History to Improve Building Energy Demand Predictions
Dynamic Control for Commercial Building Energy Systems
DiBOSS Electric Savings from Startup, steering and rampdown
Di-BOSS™: Digital Building Operating System Solution
Optimized Preheat Recommendation 2013
CCLS SOW FOR ELSAG 2012
Elsag CAT CAIM Bioinformatics Economic Impact 2012-13
Anderson CAT CAIM Bioinformationcs Fall External Advisory Board 2013
CAT CAIM Bioinformatics Project and DiBoss TPO 2013
CAT CAIM Bioinformatics Annual Report 2013
CAT CAIM Bioinformatics Project Total Property Optimizer for the Smart Property Management System
Selex CAT BioInformatics Economic Impact 2013-14
Total Property Optimizer Update for CAT BioInformatics Project 2014
CS 4772 Advanced Machine Learning Final Project / Survey Paper-Kernel Tricks and Accelerating the Computation of Kernel Machine Classifiers
Polaris Intelligent Buildings Article DiBOSS 2013
FNM Intelligent Buildings 2013
Energy Efficiency &amp; Smart Buildings: Real Time Dynamic Demand Response in Rudin Management Company buildings in Manhattan and Verizon Buildings in Manhattan
Di-BOSS PRESS RELEASE from Finmeccanica
FINAL Di-Boss Scrolling Stand Presentation v2 REALCOM 2013
RealComm Booth Storyboard vfinal 2013
Energy Efficiency In Buildings 2004
Four Times Square
Temperature control module increases energy efciency and reduces costs in buildings \
Total Property Optimization (TPO) System Design Specifications Document-August 2014-Center for Computational Learning Systems Columbia University
Di-BOSS Economic Impact 2014
Adaptive Stochastic Controller for Total Property Optimizer Version 3.0
DiBOSS in Finmeccanica Focus Magazine July, 2013
DiBoss Cost Savings thru August 2013
Di-­-BOSS Digital Building Opera2ng System Solu2on Executive View Tenants Building Managers Poster
Di-BOSS™ Digital Building Operating System Solution Brochure from Finmeccanica
Innovative Building Operating System Provides the Brain for Smarter Cities
Anderson, RN Vita 06012020
Rudin Management to Roll out Energy Saving Di-Boss(TM) Building Operating System after Successful Pilot Study
Machine Learning Software for Optimization of Building Energy usage
Temperature control module increases energy efficiency and reduces costs in Smart Buildings
IBCon 2013 Keynote Panel with John Gilbert of Rudin Management
DiBOSS Demo Short
Preheating Recommendations within the Columbia University Total Property Optimizer for Winter Operations of High-Rise Office Buildings
Description of the Methodology in TPO Alpha for Start-­-up and Ramp down Recommendations
Now-­-Cast Recommendations within the Columbia University Total Property Optimizer (TPO) for Steering Space Temperatures for Optimal Operations of High-­-Rise Office Buildings
Prediction and optimization of energy consumption at single-building or district-scale
Di-BOSS™: Digital Building Operating System Solution
TechReport Secure Internet Protocol Smart Adapter for Communicating and Optimizing Building Power Management within the Smart Grid 2013.
Di-BOSS™: Digital Building Operating System Solution
PRESS RELEASE Brewster, NY-November XX, 2013 SELEX ES LAUNCHES GROUNDBREAKING &quot;TENANT FRACTAL&quot; COMPONENT TO THEIR DI-BOSS™ DIGITAL BUILDING OPERATING SYSTEM
Worldwide Large Building Intelligent Management Business 2012
Center for Advanced Information Management SMART BUILDINGS TOTAL PROPERTY OPTIMIZER 2013
Computer Aided Information Management and DiBOSS Total Property Optimizer 2014
Announcing Rudin Management the 2014 Digital Innovation Award Winner for Commercial Real Estate
DiBoss User Manual 2015
Intelligent Buildings DiBOSS Digital Building Operating System for Large Residential Buildings 2013
Intelligent Buildings DiBOSS Digital Operating System for Commercial Bulidings 2015
Goals: Operations Energy Efficiency Sustainability Tenant Experience Financial Optimization C H A L L E N G E S : Rudin NYC Residential Portfolio Rudin Management, Prescriptive Data Combining the power of DiBOSS and the Intel® Gateway Solutions for IoT to Enhance Building Intelligence in the Residential Environment
Di-BOSS The Digital Building Operating System of the Future Buildings Finally Get a Brain 2015
Large Scale Energy Efficiency Improvements in New York City and other Vertical Cities 2015
TPOCOM: Process Flow for Send and Receive Components 2013
Description of the Methodology in TPO for Start-up and Ramp down Building Recommendations 2013
Description of Preheating Recommendations within TPO 2013
Now-­-Cast Recommendations within the Columbia University Total Property Optimizer (TPO) for Steering Space Temperatures for Optimal Operations of High-­-Rise Office Buildings
Advantages of Ensemble Learning Model Based on Hidden Markov Model (HMM) for TPO v2, 2015
Research Report: Improvement in TPO Forecast Performance and Automation using Ensemble Methods and Unsupervised Learning
Retrospective study comparing actual Skyscraper building data to the recommendations of the CCLS Adaptive Stochastic Controller, for the coldest part of the year 2014
TPO Design Specification Document June 2015
DiBOSS Brochure for Realcomm 2015
CTBUH: BUILDINGS FINALLY GET A BRAIN
Building is the Network Chapter 14
Building is the Network Chapter 11 Sandy and 80 Pine Transformation
Buikding is the Network Chapter 13 From Smart Cities to Planet Inspired
Building is the Network Chapter 12: From Di-­-BOSS to Smart Cities
Buiding is the Network Chapter 10 Di-BOSS = Brain and OS
Building is the Network Chapter 8 345 Park and the Testbed
Building is the Network Chapter 7: Blackout of 2003 and the Smart Grid Project
Building is the Network Chapter 9: Internet of Metcalfe and Power of Tesla/Edison joined by the Brain of Kandel via Machine Learning
Di-BOSS Research, Development &amp; Deployment of the world’s first Digital Building Operating System
Di-BOSS Stump Speech v30 10052013 Chapter
Ensemble Machine Learning System Internal CCLS Report
Di#BOSS Research, Development &amp; Deployment of the world’s first Digital Building Operating System
20180709 CALM Energy
Finmeccanica Planet Inspired in the Di-Boss Partnership
Rudin 3
ENERGY, GENERAL EARTH INSTITUTE Di-BOSS: the World's First Digital Building Operating System
TOTAL PROPERTY OPTIMIZATION SYSTEM FOR ENERGY EFFICIENCY AND SMART BUILDINGS
Predictive building management system for improved energy efciency in smart buildings
Improving efficiency and reliability of building systems using machine learning and automated online evaluation
ADAPTIVE STOCHASTIC CONTROLLER FOR ENERGY EFFICIENCY AND SMART BUILDINGS
Refinement of a Support Vector Machine Regression Model for Forecasting Commercial Building Energy Loads: A Use-Phase Approach to Building Energy Efficiency
US Patent 8036996 SYSTEMS AND METHODS FOR MARTINGALE BOOSTING IN MACHINE LEARNING
Forecasting energy demand in large commercial buildings using support vector machine regression
Adaptive Stochastic Control for the Smart Grid
Using Support Vector Machine to Forecast Energy Usage of a Manhattan Skyscraper
Systems and methods for martingale boosting in machine learning
CCLS License- ADAPTIVE STOCHASTIC CONTROLLER FOR ENERGY EFFICIENCY AND SMART BUILDINGS
CCLS License: FORECASTING SYSTEM USING MACHINE LEARNING AND ENSEMBLE METHODS
CCLS License TOTAL PROPERTY OPTIMIZATION SYSTEM FOR ENERGY EFFICIENCY AND SMART BUILDINGS
Automated Diagnostics and Analytics for Buildings Ch 9 Di-BOSS
Predictive building management system for improved energy efciency in smart buildings HIGHLIGHTS Inventors
Estimation of System Reliability Using a Semiparametric Model
Failure Analysis of the New York City Power Grid
Improving Efficiency and Reliability of Building Systems Using Machine Learning and Automated Online Evaluation
Innervated stochastic controller for real time business decision-making support