Smart Buildings through Artificial Intelligence

AI in Smart Buildings

Published by FirstAlign

On the outside, every building is made of bricks and mortar, but with advanced technology, we can create smart buildings. The global smart building market that was valued at USD 7.0 billion in 2014 is expected to reach 36 billion US dollars by 2020.

Today, buildings generate tons of data. Initially, it was only data from sensors and cameras, but with IoT devices, we can get meaningful data from every corner of the building. The question is, how is data of any value if we cannot derive meaning out of it?

That’s exactly what Artificial Intelligence (AI) does. It processes data from various sources and analyzes it to provide valuable operational insights. Insights that improve efficiency and better utilization of assets. Let us look at ways AI can transform buildings into “Smart Buildings”.

Improve energy efficiency

Managing a building’s energy requirements efficiently is the most important part of transforming a building into a smart one. i-REAP is a system that is being developed by the University of West of England (UWE Bristol). i-REAP stands for IoT-enabled Real-time Energy Analytics Platform. It is a collaborative R&D project funded by the Department for Business, Energy and Industrial Strategy. TerOpta, an engineering firm is developing IoT enabled sensors for i-REAP and Costain is the project partner providing five sites across the UK. i-REAP aims at increasing the adoption of Artificial Intelligence and IoT in buildings for energy-saving. It helps the building sector move towards predictive approaches.

The general approach for the efficient use of energy and saving costs is done based on after-the-fact reporting. Analyzing what energy was used for and implementing changes for the next time. i-REAP is a one-stop solution that can measure, predict, and optimize energy consumption using the latest AI and IoT based software.

For example, to optimize the cooling and heating operations of a building it uses a; measure, predict, and optimization approach. IoT sensors capture real-time changes in temperature, energy consumption, and user occupancy. It then interrogates the data gained with other information such as weather, equipment specifications, and the architectural design of the building.

Based on compiled data, i-REAP predicts future energy consumption patterns. It uses Neural Networks and in particular Long Short-Term Memory Neural Networks for the process. Novel evolutionary and deterministic optimization models will use the predicted energy consumption patterns to optimize and control the operation of the heating and cooling equipment. With time, i-REAP systems can identify elements responsible for thermal loss. This data is used to develop guidelines for the ideal retrofitting of the building.

Water management

Water is a precious natural resource that we need to manage efficiently. An average person in the United States uses 300 gallons of water daily at home. Imagine the global water consumption scale. Can smart buildings manage water more efficiently? Here is where AI can be of significant benefit.

AI works with large data sets. It improves itself with each set of data. This adaptability of AI makes it ideal for predicting  changes and creating new insights. 

AI tools capture and analyse the data from sensors embedded into water supply channels. It checks if any family or office is consuming more water than expected levels. The AI tool then sends this report to the building manager/ housing association or owner to take proper action.

Artificial Intelligence detects leaks in pipes thereby conserving water and preventing damage to buildings. Machine Learning is used to understand and analyse water flow patterns. Flow patterns are dependent on location and seasons. Once normal patterns are established AI based systems can detect leaks and provide real time alerts.

Another way of using Artificial intelligence for water management is to detect faulty meters. San Francisco–based Valor Water Analytics uses the ML algorithm to detect inaccuracies in meter data. It also provides utilities with tools that they can use to work with customers to reduce water use and identify leaks.

Fault prediction

Building maintenance is an ongoing activity that can become a major task based on the complexity of the building. AI technology analyses inputs, study patterns and detects faults before they can even occur. For example, AI tools can process information captured by sensors and cameras in a lift/ elevator to detect if there is a chance of a breakdown. Based on AI analysis, the building manager can act proactively.

BuildingIQ, a US-based company, has developed an Outcome-based Fault Detection (OFD) system. OFD is a building management service that helps identify issues, prioritize faults, and validate work done. OFD works on a combination of human monitoring, industry-standard rules, and advanced ML analytics.

Conclusion

Smart Buildings” is an evolving transformation that involves Artificial Intelligence technology. Sensors and cameras alone cannot make buildings smart. Adopting AI technology is what can truly transform buildings. Smart buildings efficiently manage energy and water. They are proactive in preventing faults. It reduces costs, enhances occupants experience, utilizes assets effectively. Efficient buildings help us progress towards sustainable development. Now, that’s what we can truly call SMART!

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