The aim of the project is to develop an integrated, robotic, autonomous building management system. The system will enable the integration with other BMS management subsystems based on machine learning technologies and artificial intelligence dedicated to automating decisions related to building management.

The created tool will allow to monitor the building life cycle, make decisions, dynamically change the operational parameters in response to changes in the environment and events occurring inside the building. Due to the current observed delays of such decisions in existing management subsystems, the proposed monitoring of the new system also considers the anticipation and forecasting of the life process taking place inside and around the building.

 

iBrain

 

DEVELOPMENT OF A CENTRAL CONTROL SYSTEM TO THE MANAGEMENT OF BUILDING’S AUTOMATION

​▪ SYSTEMS.
SYSTEM DESIGNED AS A SOFTWARE BASED ON MACHINE LEARNING TECHNOLOGIES AND ARTIFICIAL ​

INTELLIGENCE DEDICATED TO AUTOMATING DECISIONS. 
REDUCING THE ENERGY CONSUMPTION OF THE BUILDING WHILE INCREASING THE USER'S COMFORT
▪INCREASE THE PRODUCTIVITY AND INVOLVEMENT OF USERS BY AUTOMATING THE BEHAVIOR OF THE ​

BUILDING AND INCREASE THEIR SATISFACTION
REDUCING THE ENERGY CONSUMPTION OF THE BUILDING WHILE INCREASING THE USER'S COMFORT
REDUCING THE COSTS OF MAINTENANCE AND DIAGNOSTIC WORK
INCREASE THE RELIABILITY OF DEVICES BY AUTOMATICALLY CONTROLLING THEIR LOAD

Key goals

 

Milestones

 

COLLECTING DATA ABOUT PREFERENCES AND BEHAVIOR OF USERS IN THE USE OF BUILDING SUBSYSTEMS
▪ ESTIMATION OF PARAMETERS OF MACHINE LEARNING (ML) METHODS AND ARTIFICIAL

​▪ INTELLIGENCE (AI) ALGORITHMS FOR THE SEPARATION OF RULES AND PROFILES OF USERS ​USING THE BUILDING
▪ USING MULTI-AGENT SIMULATION TECHNOLOGIES TO OPTIMIZE THE OPERATION OF ​
INDIVIDUAL

SYSTEMS DUE TO THE DEFINED OBJECTIVE FUNCTION.
USING SIMULATION METHODS AND OPTIMIZATION RESULTS TO FORECAST AND USE TECHNICAL
INFRASTRUCTURE
USING "REINFORCEMENT LEARNING" TO OBTAIN USER RESPONSE PROFILES FOR AUTONOMOUS
CONTROL
▪ USING MULTI-AGENT SIMULATION TECHNOLOGIES TO OPTIMIZE THE OPERATION OF ​
INDIVIDUAL ​SYSTEMS 
▪ SCALABILITY OF THE SYSTEM CREATES DATA WHICH IMPROVES FURTHER DECISIONS IN ​
FACILITY 
MANAGEMENT (FM) PROCESS – IT’S CREATED BIG DATA CONNECTED WITH FM MARKET

Responsibilities

Salvage develop a central control system designed as a software based on machine learning technologies and artificial intelligence dedicated to automating decisions related to the management of building’s automation systems, where most important responsibilities will be:

 

▪ JOINING EXISTING TOOLS TO INTEGRATED BUILDING MANAGEMENT SYSTEM (IBMS) 

▪ COMPUTER AIDED INTEGRATED FACILITY MANAGEMENT SYSTEM (IFM)

▪ BUSINESS MODEL AND IMPLEMENTATION STRATEGY

▪ CREATING AN ENVIRONMENT FOR FACILITY MANAGEMENT SOFTWARE TOOLS BY API

Product strategy consideration

▪ SCALABILITY OF THE SYSTEM CREATES DATA WHICH IMPROVES FURTHER DECISIONS IN FACILITY

▪ MANAGEMENT (FM) PROCESS – IT’S CREATED BIG DATA CONNECTED WITH FM MARKET.

▪ BUSINESS PRODUCTIVITY.

▪ PLACE EXPERIENCE AND INTERACTION

▪ ANYTHING AS A SERVICE (XAAS)

▪ SUSTAINABILITY IN LIFE CYCLE

▪ ENERGY MANAGEMENT

▪ PERFORMANCE CONTRACTING

▪ COLLABORATION AND MOBILITY

▪ HEALTHIER AND SMARTER SPACES

▪ PRESTIGE