Life cycle costing (LCC) analysis forms the fundamental tenet of assessing sustainability initiatives and projects, and the MEP industry is not immune from this yardstick.
Be it chillers or pumps, LCC analysis has become almost mandatory for equipment selection. LCC analysis also indicates that the O&M costs through the life cycle of MEP equipment are 80-90% of their LCC. Thus, the recommendation is always to opt for more energy efficient equipment even though the capex requirement may be higher compared to equipment of equivalent capacity. This is the theory.
In reality, any equipment’s/project’s true LCC is determined by the way it is operated and maintained. If carried out well, the O&M cost of an energy inefficient equipment could be lower than that of an equivalent energy efficient equipment which is not maintained properly. So, while FM contracts stipulate KPIs and SLAs that largely monitor the ‘quantity’ of maintenance, supervising the ‘quality’ of maintenance, especially for personnel overseeing millions of sq. ft. of built-up space, can be physically challenging. Thus, over a period of time, in spite of existing maintenance contracts, condition of equipment deteriorate beyond what is normally expected, leading to higher energy costs and/or reduced equipment life times.
Artificial Intelligence (AI) can help solve this problem, by combining multiple technology and supervision paradigms like IoT, Big Data and Subject Matter Expertise (SME) and by bridging the gap between data that is authentic, reliable and timely, and SMEs, who can then analyse the data accurately and enable Just-in-Time actions. AI can also complement the work of SMEs, by carrying out analyses on their behalf. In principle this sounds logical and simple. Unfortunately in real life situations when one is faced with hundreds and thousands of equipment, generating millions of data points, sifting through all the data and separating the ‘wheat from the chaff’ could become a near impossible task. Automation to a great extent resolves this challenge. However, ensuring that actual site conditions indeed match on-screen data could require sizeable amount of physical verification. This is where AI can be utilised effectively to arrive at holistic solutions that also cover equipment that are not connected to automation systems.
Collecting data that require physical verification can, not only be time consuming but can also be prone to errors and fraud. An SME sitting inside a Command Control Centre (CCC) may be aware of the performance of the chilled water system, but may not realise that the building has an inherent risk that can be triggered by an FCU located on top of a server. Alternatively, a poor quality maintenance work on an FAHU motor could trigger a subsequent shut down of the equipment, causing occupant discomfort and customer dissonance, but missed by the SME in the absence of physical verification. From a building’s or a community’s security perspective, visitors and service providers are by and large tracked manually only at entry points, which is again a time (and manpower cost) consuming repetitive activity.
Monitoring such diverse set of issues requires ongoing collection of physical evidence, in an authentic manner and then transferring this data onto central repository where it can be analysed by a combination of SMEs and AI. Collecting the data in a way that is indisputable is the first step and this can be achieved through digital cameras, either in the form of smart phone cameras, drone cameras or fixed digital CCTV cameras. This allows the photos to be time and location stamped automatically and uploaded to the AI platform over a cloud. AI platform can then be used to automatically identify existence of equipment/ people, count and recognise; automatically creating asset registers, which is otherwise a manual, time-consuming and un-verifiable process.
AI can also be used for conducting site audits of various types – fire safety, equipment condition, building risk etc. AI allows large amount of authentic data to be collected and analysed in an efficient and accurate manner, and creation of comprehensive asset registers automatically. In addition, such visual information enables SMEs to review equipment condition accurately and quickly, based on which intelligent decisions can be taken. Due to this modus operandi, AI helps minimise, or eliminate, disputes between concerned parties, helping improve contract management and supervision. For secure access of properties, AI allows seamless movement of registered personnel, eliminating costly attendance systems like fingerprint scanners, manual registers, access card scanners etc. Akin to Salik, AI enables people to enter and exit buildings/ communities without having to stop, except in special circumstances. Such AI platforms can also be integrated into the automation systems to operate MEP equipment in efficient manner. Thus, AI for security can ubiquitously work in tandem with MEP systems to increase or decrease cooling, fresh air and lighting based on number of persons inside a building.
Artificial Intelligence is being increasingly used to prevent incompetence, fraud and wastage of manpower resources; thereby improving performance at lower operating costs.