Dublin, December 28, 2022 (GLOBE NEWSWIRE) – The report “AI and maintenance forecasts in smart buildings” is added to Of ResearchAndMarkets.com Offer.
This research provides an in-depth look at maintenance, forecasting and AI in smart buildings. Using stakeholder surveys, expert interviews, and detailed market analysis, this project is designed to understand how consumer, customer, purchasing, and interactions of all ecosystems influence and influence the development of these technologies. .
Overview of AI and predictive maintenance in buildings
The wave of software that will use AI and machine learning (ML) to automate basic work will disrupt any industry imaginable. Smart building is no exception, bringing with it major applications in the areas of maintenance, energy management, financial analysis and experience management.
Predictive maintenance depends on reaction analysis as well as multidirectional and convolutional neural networks (CNNs). Regression analysis is a form of controlled ML that predicts the effect that one variable has on another variable based on how the two variables interact.
CNNs also rely on controlled MLs, but are specifically designed for image recognition. Predictive maintenance can be characterized as a suite of software and platform tools that use data from network management and automation systems, distributed sensors, and external business intelligence to provide signals from real-time voice estimation. The system is expected to crash.
The growing need for greater visibility and control around systems and machine health, along with the rise of emerging technologies, has led to a cycle of innovation and consistent progress around predictive maintenance. This method effectively identifies the probable problem and estimates the lifespan of the system given the occurrence of that problem.
Most AI applications for in-building forecast maintenance aim to reduce labor costs, downtime, and the entire length of the maintenance process. This is largely achieved by predicting possible system failures and sending technicians before such failures occur. Doing so is likely to result in less time spent analyzing problems and less costly dollars to replace machinery. Which can be fixed.
Key topics covered:
1. Introduction: The evolution of AI and predictive maintenance in smart buildings
1.1 What is a smart building and how did it evolve over time?
1.1.1 Evolution of smart buildings
1.1.2 Introduction to Smart Systems
1.1.3 Evolution of management, maintenance, best practices and technologies
1.1.4 Maturity of construction technology determines the stages for artificial intelligence
1.2 Estimates of maintenance requirements of buildings vary greatly by building type
1.3.1 Stage-based technology innovations for intelligent system applications
1.3.2 Maturity of ecosystems leads to complex business models and data sharing practices
1.3.3 Potential for additional locks affecting demand distribution by building type
1.4 Types of buildings and case studies
1.4.3 Retail and Hospitality
1.4.4 Main Mission
1.4.5 Public places
2. Artificial intelligence is launching a new generation of predictive care software
2.1 Overview of artificial intelligence and machine learning
2.1.1 Smart Building Data Pipeline
2.1.2 Artificial intelligence in building player ecosystems
2.2 Use cases for AI in smart buildings
2.2.1 Maintaining AI-enabled Predictions
2.2.2 Power Analysis and Optimization with AI
2.2.3 Optimizing and automating building operations with AI
2.3 Launching AI-Powered Intelligent Building
2.3.1 Integrating AI with Building Automation
2.3.2 Data security and privacy must be considered
2.3.3 The role of AI in Net-Zero Future
3 Achieving the promised value of forecast maintenance
3.1 Tenant satisfaction is linked to how well the building manages maintenance needs
3.1.1 Assurance of maintenance as a factor affecting satisfaction
3.1.2 Pain points and expectations surrounding building maintenance
3.2 Building operators recognize the need for smart maintenance management practices
3.2.1 Operators’ frustration with building maintenance practices
3.2.2 Promising sensors and data collection practices
3.2.3 Price proposal that moves the needle
3.3 Certain fears must be overcome first
3.3.1 Operators concerned about work impact
3.3.2 Tenant Fear Related to Accepting AI
3.3.3 Supplier strategies to overcome AI barriers and market catalysts
3.4 The willingness of owners and operators to pay provides opportunities for AI and PMIB ecosystem participants
3.4.1 Operator willingness to increase costs with operating benefits
3.4.2 The tenant is willing to pay, allowing the building to be paid for by increasing the rent.
3.5 Eliminating barriers to adoption
3.5.1 Expenditure on stimulus activities
3.5.2 System integration and data management are costly barriers
4. Predictive maintenance in smart buildings
4.1 Predicting maintenance solutions, instructions and overview
4.1.1 Components and capabilities of existing PMIB solutions
4.1.2 Supplier landscape and evolution of PMIB market
4.1.3 Functions and features of the forecast analysis system
4.2 Interaction between maintenance, forecasting and components of intelligent building.
4.2.1 Air heating and air conditioning systems
4.2.2 Power Management
4.2.3 Holder comfort system (lighting, shading)
4.2.4 Water management.
4.2.5 Network and communication infrastructure
4.2.6 Elevators and elevators
4.2.7 Power Distribution Uninterruptible Power Supply (UPS) and Failure / Disaster Recovery
4.2.8 Structural Integrity
4.3 Technology is consolidating to enable forecast maintenance
4.3.2 Secure remote access
4.3.3 Digital Twins
4.3.4 Edge calculation
4.3.5 Online Security
5. Provide a seamless experience
5.1 Throughout the PMIB price chain, players must take action now
5.1.1 OEM Strategic Guide
5.1.2 Provider Strategic Guidance
5.1.3 Provider Strategic Recommendations
5.1.4 Recommendations for building owners / property managers
5.2 Interoperability and collaboration are catalysts
5.3 Conclusion of AI and PMIB Report
For more information on this report, please visit https://www.researchandmarkets.com/r/p0tay7
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