Smart buildings integrate various systems to effectively manage resources in a coordinated manner in order to maximize technical performance, operating cost savings and tenant comfort. These buildings are expected to extend beyond simple automation to include advanced user interfaces, and automatic building management capable of interacting in real-time. It is not yet clear, however, how to design and implement applications with the entire building structure, services and processes. We discuss the importance of considering context in the operation of smart buildings, and present context-based reasoning as a modeling paradigm to create a general purpose applications.
Indicators show that there is a high cost-effective potential for energy savings
in buildings [
SBs are supported by a number of technologies, included in the automated building management system (BMS), that aim at the well being of occupants, promoting a comfortable environment while ensuring an efficient use of building resources.
The ideas described for SBs fall into a wider concept defined as Ambient
Intelligence (AmI) [
The deployment of AmI in buildings has been hindered, not only by the lack of
a well defined and globally accepted standard to interconnect building systems,
but also by the absence of a common platform that organizes all these different
systems with associated knowledge, control strategies, services, variables,
models, etc. A SB is a very complex system [
Most software architectures for SBs are programmed in a modular way. This modularity deals with the complexity of the BMS’s domain by dividing its operation into a number of interdependent services that are able to control building systems and functions such as: lighting, HVAC, access control, roomoperations, floor-operations, etc. These modules, responsible for each control logic, are largely deployed in isolation and do not take into account a great deal of contextual information that could be useful for their operation. For example, an elevator group scheduler could balance between energy efficiency and quality of service (associated with the expected waiting times), depending e.g., on a holiday or a normal working day.
In this paper we discusses another type of modularity: the operation of each service depends on a set of active contexts. These contexts organize knowledge and the necessary reasoning mechanisms to act on the buildings in order to accomplish greater energy savings than the ones we would accomplish with simple automation rules. 1.2
The awareness of context about the environment, discussion, or problem in hand, allows many important aspects of human interaction to remain implicit. Contexts act like adjustable filters creating a knowledge frame that enables the correct semantics to be assigned to terms therefore enabling a minimal amount of information exchange towards effective communication. This means defining, at each step, which knowledge pieces must be taken into account explicitly (contextualized knowledge) and which pieces are not directly necessary or already shared (contextual knowledge). Human communication uses linguistic expressions that are rather highly contextualized and many misunderstandings, in human discourse, take place when communicants are not in a common context. A context inherently contains much knowledge about a situation and environment of a problem. For example, an area in a supermarket, where temperature values are abnormally different from the rest of the building, correspond, with high probability, to the cold section. In another type of service building, a similar situation may correspond to a datacenter.
In the next section we present some of the related work on applications for SBs. In section 3 we discuss the organization of knowledge and strategies and in section 4, how context-based reasoning (CxBR) can be used to organize such knowledge. Section 5 clarifies the concept of context and how it can be applied in different building services. Section 6 concludes. 2
Creating applications for SBs is a current topic of research. Most approaches
have used decentralized control solutions based on multi-agent systems (MAS)
see, e.g. [
Even though a lot of research has been conducted within context-aware
systems, the core term context is not yet a well defined concept [
The organization of knowledge (e.g. how energy is used in a certain room), and planning strategies based on that knowledge (that fulfil some expectations like e.g., saving energy) is not an easy task. It should be accomplished in a modular way and should be available where it is needed i.e., global knowledge (type of building, season o the year, etc), and knowledge associated with events in a certain area (e.g. the schedule of a tenant), should be available for decision making in that area. In this organization we have to consider: { Pre-acquired knowledge. Of a static nature associated with a building and its operation that needs to be known before deploying a BMS. This includes knowledge about: architectural aspects like the buildings’ location and a plan of its structure including doors, rooms, materials, glazing, furniture, electrical layout, pipes, etc; building systems (with information on service providers) such as elevators, HVAC (including subsystems, ducts and vents), power storage and generation, sensors and actuators, etc; the building’s function (supermarket, pool, school, etc) and associated information like schedules (e.g. holidays, working days), description of spaces (amphitheater, classroom, kitchen, etc), and other information like: a company or a department occupies a specific part of the building; occupant’s activities, and the association of these activities with specific spaces inside the building (sleeping, working, eating, entertaining, etc); electric and gas utility rates. { Acquired knowledge. Accomplished through a process of gathering information from the environment to improve the efficiency of a system in achieving certain goals. This includes creating models that can be used to predict and anticipate the behaviour of tenants and explain variables like indoor temperature, power, lighting, humidity, thermal-behavior of spaces, etc. There are many types of algorithms and techniques that can be used for this purpose. The learning process is performed throughout the operation 3 Throughout this paper we will use the term multi-service.
of the building, with the models being continuously adapted and fitted to the observations. A well-defined organization of knowledge must take into consideration the context (e.g. holidays, working-days, winter, summer) that help explain these variables (e.g. the total amount of energy used over those periods). { Operation strategies (including optimization). Technical difficulties in creating SBs also include the fact that the set of all possible behaviors, given all possible inputs, is significantly large. It can also be from dealing with several different types of data (discrete/real valued, complex-structured, states, transitions, etc) and multiple goals (e.g. energy efficiency and comfort) depending on context (working hours, holidays, emergency, etc). Operation strategies can also be partitioned into a hierarchy of levels and contexts. For example, at the highest operation level of a BMS, a building manager can be informed that energy is being lost because the building is not sufficiently airtight (with detailed information); or some operational parameters of a chiller can be adjusted. At a lower (or local) level, a window can be closed because the HVAC is on. Some local decisions may depend on higher level strategies: e.g. a smart thermostat in a room will not turn the cooling/heating on/off, if the HVAC system is powered down, after a certain hour, in certain weather conditions.
To avoid ending up with a data rich but information limited environment, conceptual modeling of information must be part of the engineering process, to describe the general knowledge of each domain (HVAC, elevator, room, company located on the 5th floor, etc). Conceptual models serve to organize information in a way that can also help e.g., system operators understand the full context of some type of event that is occurring in some part of a network or process. This organization is necessary to support the ability to provide the right information at the right moment to the right decision maker. For example, if something is wrong with the HVAC system, then a message can be sent to an entity responsible for managing this system with detailed information. High-level contextualized information services are often needed along with supportive sensor data or trends to provide context e.g., a malfunction X in the HVAC happened due to a situation Y, as shown by some sensor values Z. The goal is also to facilitate data mining, information publishing, and the application of automatic learning and decision support tools to facilitate system management. For example, a room management service can learn that energy is being wasted when a window opened, while the HVAC is on. If such a situation happens frequently, the service may point out that fact by emailing the tenant with detailed information about how much energy is being wasted. At the building level, a building manager can be informed on how much energy is lost in the entire building due to to opened windows, including the corresponding economical costs.
The concept of context can provide a model to partition the operation of a
complex system into “scenarios”, where knowledge, strategies, parameters and
objectives, are organized. To clarify the concept, lets consider the use of context
in the following applications:
{ Problem diagnosis. In problem diagnosis, context can be used, for
example, to reduce the search-space when trying to detect the source of an
identified problem. Gonzalez et al. [
Part of the architectural design of a building service (e.g., a service that manages
the operation of a room by controlling the HVAC and lighting) is designing the
CxBR model, i.e., identifying the context set(s), transition rules, dependencies
and relations between contexts. The classical frame problem [
For buildings, this can be used, e.g., to include some of the pre-acquired knowledge, suited for the context. 5.1
A CxBR model can include a context hierarchy as shown in Figure 1. The model can be used to partition knowledge into sub-levels, making it available in the context where it “makes sense”. A multi-level hierarchy represents a vertical relationship between groups in a set G = fg1; : : : ; gng. A group gi 2 G contains a set of mutually exclusive contexts Ci = fci0; : : : ; cing and, an active context cia in gi, is active within the context of its parents i.e. it will inherit active, transitional and declarative knowledge from selected contexts in groups that are hierarchically above gi. cia can redefine or specialize behaviors and/or contain the functionality required to perform specific sub-tasks.
g1 g2 g3 g4 g5 gn
Buildings
Educational Winter
Spring
Summer
Autumn Holiday
Weekend
...
Night
Dawn
Day
Dusk Exercising the CxBR model is the process of activating the set of contexts that best suits the situation in hand. This activation allows the active contexts to take over and control the execution a process, defining behaviors, constraints, and other context-dependent characteristics. The process must survey the environment as well as its internal state (including transition knowledge) to determine the conditions where the current context is deactivated and a new context is activated. In Figure 1, if context “Night” is activated then, following an hypothetical scenario, contexts “Holiday”, “Winter”, “Educational” and “Building” are also Catctive: activated i.e., the entire path up to the root of the hierarchy tree. A context can override behaviors, add behaviors, redefine attribute values and add knowledge to what it inherits from its parent contexts. Activating the correct context within some processes can be a hard problem. A process that manages the operation of an office room, e.g., may be directly associated with an observable or partially observable state composed by the set of variables that are important for the operation of that room: (door/window opened/closed, temperature, humidity, ocuppied/empty, etc). The temperature inside the room behaves differently if a door/window is opend/closed or if the room is empty or occupied. In such a situation, context can be defined e.g., by a set of explanatory variables that can somehow be used to explain or to predict changes in the values of other variables of the state.
G exists within the domain of a service s. At certain instance t, there is a set Catctive = (c1a; : : : ; can) that contains all the active contexts that exist in G. This set is continuously updated, as the following example shows: Catctive = fBuildings; Educational; Spring; Holiday; Duskg
# Cat+ct1ive = fBuildings; Educational; Spring; W orkingDay; N ightg Service si has its own execution thread(s) and its control is a function of Control of si = (Catctive) where is the CxBR framework operating within si. Figure 2 shows a representation of the framework, including inputs and outputs.
Inputs to the Process CxBR Framework
Transition
Knowledge Inference Engine
Cactive Action knowledge si
Declarative
Knowledge
Outputs/Actions
Distributed applications for a BMS can be composed by multi-distributed context-aware services. The interaction/inter-dependency between these services can be represented by a directed graph. The elements of the graph belong to the set of services S = fs1; : : : ; sng that operate with the BMS and the edges represent some type of context or action dependency. Figure 3 shows an example that includes services to manage a building-central (e.g., one that contains the set of contexts represented in Figure 1), a floor, a department and two rooms. Room X
Room Y
Most actions assumed at the highest level (in the graph, probably the most connected vertex) affect the operation of all services: if the HVAC is turned off, then there can be no room-level HVAC strategies in operation within any other service. Most information and knowledge that exists within this service, can also used by several others: season of the year, building characteristics, etc.
Behaviors of a room-service can depend on a floor-level strategy or on other information like e.g., information specific to a certain department of a company that is located at that building. For example, it may make sense to turn the HVAC off if a department meeting is scheduled to happen on another room. The operation of a floor-service can depend on the current context of each room on that floor. To model, e.g., the thermal-behavior of all spaces, within that floor, it will need to know if windows or doors are opened/closed and the temperature/pressure difference between those spaces. 6
We need the necessary foundations to acquire and organize knowledge and create the necessary reasoning mechanisms to act on the building and accomplish greater energy savings than the ones we could accomplish with simple automation rules. A building is a large complex system and there has been no common platform that organizes all these different systems with associated knowledge, control strategies, services, information, variables, models, etc.
In the last few years frameworks like the Robot Operating System 4 have been introduced to the robotics community as a common development platform for robots that provides hardware abstraction, low-level device control, implementation of commonly-used functionality message-passing between processes, etc. A similar platform is necessary for smart buildings. Such a software framework, for smart building software development, would enable programmers to reuse drivers and create optimization algorithms with an abstraction over the underlying hardware. We need a framework that is specific for buildings (that can use infrastructure/communication protocols like BACnet, Zigbee, etc) and to create such a platform, we have to know how to cope with the dimension of the system and consider the heterogeneity and complexity of a building environment. 4 http://www.ros.org/wiki/
In this paper we discussed the importance of using a context-based architecture to support some of the aforementioned requirements that are necessary to create smart buildings. We proposed a modeling paradigm that needs to be elaborated and tested. Our vision includes working on a framework similar to the robot operating system, but for buildings. A clear strategy on how to structure such a operating system to fit a building environment and building management requirements is needed. We believe that this vision of creating a building operation system has a lot to gain with previous work on software architectures for context-aware applications.
This material is based on work supported under a Portuguese National Science and Technology Foundation Graduate Research Fellowship, by FCT grant number SFRH/BD/60481/2009. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author and do not necessarily reflect the views of the National Science and Technology Foundation, or the Portuguese government.
We would like to thank all the reviewers for their inputs.