Utilizing big data and analytics to improve energy efficiency and responsibility in properties
In brief, big data means data dispersed in various parties’ data systems within the property environment, changing and being updated constantly. The data is entirely unorganized with regard to its intended purpose. So there is data, but it is challenging to acquire and use easily via traditional methods.
Integration gathers all data in a single location
In almost all cases, data can be made available using integrations. Integrations between automation and metering systems are usually real-time (1/s). Integrations between data systems often function more slowly (1/h, 1/d).
There must also be a place to store the data which is able to receive large quantities of data rapidly and effectively. Data storage also organizes the data for analytics so that it is rapidly available for use. It also takes care of data back-up. The data storage is efficient database software designed precisely for this purpose.
Analytics turns data into usable information
Using analytics we can combine data from different sources and use it to calculate information required for decision-making. Analytics can be entirely real-time and study e.g. whether or not the automation control circuits are functioning correctly, whether or not consumption figures or metering data are sensible, whether the schedules for automation or lighting control systems are set sensibly, whether the heat recovery system’s efficiency or GSHP COP/SCOP readings are correct for the relevant conditions and application, etc. When it comes to energy efficiency or indoor air conditions, the closer to real-time the analytics are, the faster it is possible to react to exceptions. If you are dealing with only one property, these functions can be carried out using modern automation. If a property owner has several properties, a centralized solution is in practice the only viable alternative purely due to costs.
For effective maintenance management, it is important to be able to analyses the alarm to find out exactly what kind of fault the alarm has detected, and what type of spare part should be taken along to the site for repairs. Maintenance must be able to automatically identify leaky water fittings, poor indoor air and the risks of damp and mould problems.
At a property level, it is important to be able to analyses realized energy consumption and condition measurements in relation to targets, and to calculate KPI key figures for effective communication on the matters. Properties can also be compared with common reference databases.
At a portfolio level, it is important to be able to compare issues relating to the energy efficiency, costs or responsibility of selected portfolios, such as shopping centers, hotels or office buildings in order to identify the state of energy efficiency, costs and responsibility for all of the property assets and compare the development of the entire property stock.
The picture below shows how even data relating to just energy efficiency and responsibility must be collected from different data sources in order to meet users’ reporting needs.
In almost all cases, data is owned by the property owner. Data ownership allows for the compilation of data in a centralized data system so that the data can be utilized.
BEMS (Building Energy Management Systems) provide a solution for the real-time analytics of raw data, helping you to react quickly to exceptions. It is possible to automatically combine the necessary data from a range of data sources to produce reports in an understandable format for decision-makers and experts.
Companies which own large properties benefit most from BEMS, since the data collection from a large set of properties takes place entirely automatically and the data is processed to support decision-making in various tasks.