Babel Buster Network Gateways: Big Features. Small Price.
Exposing Building Behaviour Using BAS
Discovering groups and structures in the data that are similar without knowing the structures of the data.
Nirosha Munasinghe MBusIT BSc BE (Hons) (Melb)
Product Development Manager,
“Each DDC in
the system shall contain a trend that will store samples of all the of
the data points” “The logged data must be sampled every 15 minutes and
archived over 2 years”. “Provide trend logging, event logging and hours
run on selected points with selectable time intervals”.
These are typical phrases from specifications. What do these mean?
Lots of data! The reduction in the price of computer memory has
allowed BAS systems to store unlimited amounts of data from the
system. What do we do with data? At present a BAS system presents
the data in the reporting tool in graphical format for the user to
interpret comparisons and report what has happened in the past and what
is happening at present. This is not enough. We need to use the
data to predict the future. This article examines typical methods to use
data to predict the future.
collection of data is useful to the daily operations of a building. A
temperature graph over a few days can illustrate room temperature
behaviours to a plant technician. The data assists the facility
department operating the building on a daily basis. Therefore it is only
operational data. To further enhance the decision making process for the
facility managers, the data needs to be fed into models to discover further intelligence. Intelligence is the ability to
learn or understand from experience and use reasoning to solve
problems. Therefore past data can be used to learn and solve future
problems. It involves acquiring data and information from a variety
of sources and modelling them for decision making. The modelled data
from the BAS system can then be a decision support system for the facility
Figure 1: Decision Support System Architecture
The decision support system can be used for tactical and strategic
decisions for the facility department. Figure 2 illustrates the data
hierarchy and types decisions that can be made with the respective data
Figure 2: Data Hierarchy
How do we implement such a system for BAS? The raw BAS data needs to be fed into a model. There are various proven data models available from a simple decision tree to more complex neural network networks modelling. A more recent development in data modelling is the concept of data mining. Data mining is a branch of computer science and artificial intelligence, where it extracts patterns from the dataset to transform data to intelligence to support the decision making process of a business. The model analyses large pools of data to find patterns and rules that can be used to guide decision making and predict future behaviour. To successfully obtain interesting patterns and behaviours requires a data warehouse. A data warehouse is a physical repository where relational data is specially organized. In a modern BAS system, there are numerous data sources with web services and open web standards and it is simple to merge large collections of data into a warehouse.
The data from the warehouse is the input to the data mining engine. The data is modelled to find patterns and correlations between fields. Some common techniques used in data mining follow:
To illustrate how data mining can assist in BAS system, let’s examine
the following dataset that consists of temperature, the operation of a
chiller, and how much energy is used in the building.
Feeding the above dataset into a data mining model can deduce following results:
The example illustrates simple association rules that can be used by
a facility department to assist the decision making process. The example
association rules just touches the surface of data mining. It will be a
powerful tool for the BAS market in the evolution of smart grid as we
attempt to make decisions on the fly to interconnect and make networks
intelligent. Also, the predictive nature can assist in reducing energy
in a building as the models can predict how much energy a building will
use for any given weather condition or day.
It can be seen from the examples that data mining can change raw
data to information and knowledge. The raw data will run the business
on a daily basis but for the business to obtain a competitive advantage
it requires information and knowledge. Therefore BAS systems need to
incorporate concepts such as data mining to assist better operation of
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