Intelligent context-awareness system for energy efficiency in smart building based on ontology

Sustainable Computing: Informatics and Systems 21 (2019) 212–233

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Sustainable Computing: Informatics and Systems journal homepage: www.elsevier.com/locate/suscom

Intelligent context-awareness system for energy efficiency in smart building based on ontology Houssem Eddine Degha ∗ , Fatima Zohra Laallam, Bachir Said Lab. Laboratoire de l’Intelligence Artificielle et des Technologies de l’Information, Fac.Faculté des nouvelles technologies de l’information et de la communication, Université Kasdi Merbah Ouargla, Ouargla 30 000, Algeria

a r t i c l e

i n f o

Article history: Received 1 September 2018 Received in revised form 21 December 2018 Accepted 29 January 2019 Available online 1 February 2019 Keywords: Smart building Energy efficiency Ontology Context-awareness Building energy management system

a b s t r a c t Context-awareness is an important research area. Many energy efficiency applications of ubiquitous computing need to access some related contexts to provide the best and adequate energy saving services at the right time and at the right place. One of the big challenges is the difficulty and the complexity to make systems identify and understand situations in the building business, especially in complex situations to provide adequate energy-saving services for each of which. This issue is divided into two parts: On one hand, the smart building and its environment must be modeled in a way that provides context-awareness. On the other hand is the how to effectively exploit this context-awareness to reduce energy consumption and increase the user comfort. In this paper, we propose an intelligent context-awareness Building Energy Management System (ICA-BEMS). ICA-BEMS uses hybrid energy-saving techniques based on a Smart Context-Awareness Management (Smart-CAM). Smart-CAM uses smart building ontology to organize smart building knowledge and utilizes a new context-awareness mechanism to provide contextual information. The former exploits the contextual information in reasoning to reduce the building energy consumption, promoting users behavioral change and maximizing the user’s comfort to bring about a better energy efficiency policy. To evaluate our system, we have developed a smart building simulator (Open-SBS) which simulates smart building comportments and human behaviors. We have created various scenarios of daily life in the smart building simulator. We have tested our ICA-BEMS effect on energy consumption having a positive result. Energy consumption has been decreased by 40% of total energy consumption. © 2019 Elsevier Inc. All rights reserved.

1. Introduction In the ubiquitous computing environment, every day technology produces new devices, services, and systems to make our daily life easier and comfortable. The building is one of the most important places in people’s everyday life. 90% of people spend most of their time in buildings [1]. Smart building is an important research area of ubiquitous computing. Knowing that global primary energy demand will grow by 31% between 2012 and 2035 and energyrelated CO2 emissions increase by 18.1% [2]. The commercial and residential building accounts for about 20% of the world’s total energy consumption [3]. The energy demand in this sector grows faster than other areas throughout some projections [3]. Energy

∗ Corresponding author. E-mail addresses: [email protected] (H.E. Degha), laallam.fatima [email protected] (F.Z. Laallam), [email protected] (B. Said). https://doi.org/10.1016/j.suscom.2019.01.013 2210-5379/© 2019 Elsevier Inc. All rights reserved.

efficiency has significant gains like saving money and good impact on the environment. Therefore several works have been conducted to saving energy [4–8]. Such as the ones made by the major Information and Communication Technology (ICT) enterprises, as IBM, MONI, Control4, and Samsung who have created their own Building Management System (BMS). In our case, we are interested in BEMS (Building Energy Management System). There are two types of BEMS: Actives and Passives. The passive methods collect environment data, devices information and past energy use in a smart building. This information is used to provide new decisions to make the best saving energy policy in the future. These methods are easy to apply. Moreover, they are faster, cheaper and better for small buildings. Active methods operate at a level of intelligence and automation. BEMS have sensors, meters, and devices to control building in real-time to reduce energy consumption. The Internet of Things (IoT) is a new paradigm that is quickly gaining ground in modern telecommunications. The central idea of IoT is the pervasive presence around us of a variety of smart

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things such as sensors, actuators, mobile phones, etc. Those things can interact with each other and cooperate with their neighbors to reach common goals. The integration of IoT technology in the building provides a better perspective to building energy management system. Anything inside the building can be a smart thing. IoT makes BEMS able to sense the environment through a large type of sensors. BEMS will be able to connect to any services through the internet like weather forecast and cloud computing services. In residential and commercial buildings, there are many energy waste causes. The most important reason is user’s behaviors and activities. To save energy there exist two types of approaches. Energy Consumption Awareness is an example of passive approaches which is based on measuring Appliance energy consumption and providing appropriate feedback. This method increases user awareness and promotes him to save energy. In Active methods, for example, the Reducing Standby Consumption is based on directly turn Appliance off when standby mode detected. Yet, both passive and active techniques do not usually take into account user requirements. Another lack of existing techniques is the difficulty of ability to understand and treat complex context in the smart building to provide the best energy saving services to reduce energy consumption cost. Additionally, for energy saving techniques, the user comfort level is a very important parameter. The disparity of requirements among each user makes the modeling of user comfort difficult. The best energy-saving system should make the best energy saving policy without effect user comfort. It is necessary to provide a new technique using both energy saving strategies (passives and actives) to benefit from their advantages. Real-time monitoring of energy consumption, among other factors, can affect up to 40% of consumption in buildings. In this sense, context-awareness systems become the ideal technology to obtain real-time information to characterize the contexts and situations in a smart building. This feature allows the system to have contextawareness about his environment to provide best and adequate energy saving services for each context in a smart building. In this paper, we will present the ICA-EBMS (Intelligent Context-Awareness Building Energy Management System). ICABEMS uses hybrid energy-saving techniques (passives and actives). It addresses avoidable issues using intelligent reasoning mechanism based on context-awareness approach. In section 2, we will start by presenting related works. Then, we define our ICA-BEMS Architecture in section 3. Section 4 is concerned with the ICA-BEMS scheduling mechanism. In section 5, we will present the implementation of our solution and results. We conclude in section 6, with a conclusion and brief discussion pointing to future works. 2. Related works A lot of methodologies and systems have been proposed to address the issue of reducing energy consumption in commercial and residential buildings [9–17]. They are divided into two main approaches: Active and Passive. 2.1. Passive approaches Passive approaches such as increasing the user’s energy awareness and providing future strategies affect and reduce the consumption of energy in building indirectly. The first and the simplest Passive approach is Energy Consumption Awareness. It enables consumers to analyze their energy consumption and provides appropriate feedback to the users to increase their awareness and encourage Eco-friendly behaviors [6]. Many companies propose their solution support Energy Consumption Awareness, Such as Google PowerMeter [7] and Microsoft Hohm [8]. In 2018, PowerMeter and Hohm API are no longer available because they are

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not sufficient to ensure energy savings in the long term. But there are some other solutions still supported like Berkeley Energy Dashboard [18]. An example of the Building energy management system architecture that supports Energy Consumption Awareness is Power-Outlets [19]. It is an energy motoring system based on the web interface. It is responsible for sending the adequate notification message to the user to increase the inhabitant awarenesson energy consumption. The architecture consists of connecting each appliance in building through a power outlet. The power outlet is a power meter that measures the energy consumption of the device and sends the acquired information to a Gateway, using a standard communication protocol (Bluetooth or ZigBee). This architecture includes a RESTful Application Programming Interface (API) [20]. The API allows users to access their energy consumption through a Web browser easily. BeyWatch project is presented by Spanish telecommunication company Telefonica. BeyWatch [21] is an innovative user-centric solution to raise energy awareness among users in order to reduce energy consumption in residential households live. E3SoHo project [22] is another work. Its focus is on making a significant reduction of energy consumption, by providing appropriate feedback on consumption to users. It also offers personalized advice for improving their energy efficiency in European social housing. A Monitoring energy consumption system has been presented by Kim et al. called SPOTLIGHT [23]. The basic idea of SPOTLIGHT project is using wireless sensor network technology (active RFID tag) for detecting proximity between users and each appliance. This proximity information’s used for energy apportionment, reporting the energy consumption profile in term of useful power of each user with each appliance (light, TV, etc.). The SPOTLIGHT provides a better understanding of an individual’s energy consumption to help people to lower their energy footprint significantly. In [24], authors utilize door and passive infrared (PIR) sensors for binary detection of occupancy for residential buildings and examine reactive and predictive control strategies for the thermostat. The predictive strategy is achieved using a Hidden Markov Model. The model estimates the probability of Building being in one of three states: unoccupied, occupied with an occupant awake and occupied with all occupants asleep. Activity scheduling [25,21] is a passive approach for energy efficiency based on determining the optimal schedule of human activity that requires appliance that consumes a higher energy or energy-hungry tasks like dishwasher or washing machine. This method reduces the cost of electricity by planning this activity or tasks when the energy fares are cheaper or the green energy production is available like solar energy. These energy efficiency passive approaches presented below have many advantages: – Increase user awareness to reduce consumption. – Easy to applaud and less expensive. – Help to change user’s behavior over time and promotes ecofriendly behaviors (good energy-saving behaviors). – It is usable for all types of buildings. – It only needs sensors to collect data from a building. And can be widely used. – Simple, efficient, and intuitive algorithms are often used to make results relatively easy to be obtained Although those pros, passive methods suffer from several shortcomings: – Does not have the ability to control devices directly to reduce consumption. – Cannot determine the cause or device or behavior responsible for the direct waste of energy. – One algorithm, to solve specific problem, may be insufficient

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– The data collected and used is the smallest that is collected in an active way and it does not allow to get advanced results. – It can’t address suddenly events that occur on the system, which may lead to waste of electricity.

– The large quantity of data used in active methods can become overwhelming to reasoning process and be inferring. – The high cost of active systems because they need many smart devices to provide the necessary services to the user and saving energy.

2.2. Active approaches Active approaches are based on the integration of the environmental sensors and actuators infrastructure in the building. These approaches come to fix the lack of passive methods. They consist to directly eliminating energy wastes contexts through control smart building appliances and actuators. Among these approaches, we find Adaptive control. It’s an active approach based on the immediate reaction with smart building appliance in real-time to reduce energy consumption. It adopts the appliances states with building and inhabitants needs. For example turning the light off in case of absence humans being, or controlling the HVAC appliances to save energy [26]. This approach is the very important Feature in building energy management systems (BEMS). Reducing Standby Consumption is another active approach. It consists to drastically eliminating energy wastes due to electrical appliances left in standby mode. Once the standby mode has been detected, the system can switch-off the device [27]. Many works in this context are proposed, as shown below, such as IPower system, ThinkHome system. eDIANA platform, AIM project... IPower system [28], combines Wireless Sensor Network (WSN) [29] and appliance control devices to provide personalized energy conservation service. IPower uses a central server interacts with heterogeneous types of sensors, actuators, and devices. C. Reinisch et al. designed the ThinkHome project [30]. ThinkHome is a multi-agent system that operates on the extensive knowledge base that stores all information needed to fulfill the goals of energy efficiency and user comfort. It uses a profile-based control strategy for thermal comfort. The system is verified by means of simulation. The primary target of ThingHome is the function that requires comparably high amounts of energy, like Lighting and HVAC systems. eDIANA is an embedded systems for energy efficient buildings [31]. It is a platform that improves energy efficiency and provides real-time energy measurement. The users are able to enter and change their preferences throughout the eDIANAplatform. In AIM project [32], the main objective is to provide a generalized method for managing, optimizing and pro filing the energy consumption of household appliances that are either powered on or in a standby state. One of older research for intelligent control of lighting is presented by Mozer et al. [33,34]. They use using neural networks soft-computing to achieve a significant energy reduction, although sometimes at expense of the occupant comfort. The energy efficiency Active approaches have many advantages: – Provide the feature of real-time controlling appliances and devices. – Capable of producing vast quantities of data that can be used to interpret subtle details. – Maximize the inhabitance comfort using automatic services execution feature. – The ability to adapt to changes in the home and deal directly to reduce energy consumption. Although those pros, the Active methods suffer from several shortcomings: – The algorithms used in this case are more complex and deal with a vast array of data. – Because of its Active approach the potential for mishaps is increased.

These approaches need to be supported by artificial intelligence (AI) algorithms. The AI methods can be used to reinforce the energy efficiency reasoning process by providing many features like detecting user presence, detection activity, learning the user preferences. One of the most interesting Learning algorithms is presented by Wei Gao et al.[35–37]. They present a partial multidividing ontology algorithm to obtaining an efficient approach to optimize the partial multi-dividing ontology learning model. The learning feature allows the system to adopt his behaviors by learning user preferences, to increase their comfort and reduce energy consumption. Both active and passive methods should follow the right energy efficiency policies without producing a negative effect on inhabitance comfort. They also do not appear entirely adequate yet because of the simple actuating system. This method also does not allow identifying the particular device or behavior causing the energy waste. And they are not sufficient to ensure energy savings for the long term. Those lacks come from difficulty to make a better understanding of contexts in building especially the complex situations to provide adequate energy-saving service for each context. And miss of efficient policy that promoting behavioral change in user that leads to energy saving. One of the best promoting solutions to address this problem is context-awareness based approaches. There are two ways to use context-awareness for energy efficiency in building. On one hand, the Passive method increases users’ awareness to encourage them to improve ecofriendly behavior for energy efficiency. On another hand, Active methods use context awareness approach to enable the system to make a better understanding of building and its environment. This aware help system to provide the best energy efficiency services automatically to eliminate waste energy contexts. Without context-awareness management, the BMS is to some extent deaf and blind and it leads the BMS to make inappropriate decision especially in complexes situations. The context-awareness makes reasoning mush easier to provide good decisions. Throughout the literature, there are many other works presented in order to optimize energy efficiency in buildings by using context-awareness technologies. [38,39]. The context-awareness based system allow for determining which factors influence the energy consumption at any time, including devices(light on, blind open, HVAC open, etc.) [38], environmental conditions (temperature, humidity, lighting, etc.) [40,41] user location [42,43], activity [39] and etc. Despite the existence of several works that have been studied for energy efficiency, we note the absence of a hybrid method that adopted both Passive and Active approach to take advantage of both techniques and strengthen their weaknesses additionally based on a context-awareness approach to address the issues of understanding contexts situation in the smart building. To provide the best energy efficiency services and promoting changing in user behaviors to saving energy, we believe that intelligence context awareness method is necessary to provide a context awareness to the building energy management systems. This method aims to provide all the required information to any service in the smart building to make the best decision and perform the right services at the right time and the right places. 3. ICA-BEMS architecture and features In this paper, we propose our new architecture for BMS systems called ICA-BEMS (Intelligent Context-Awareness Building Energy

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Fig. 1. ICA-BEMS Architecture.

Management System). ICA-BEMS is the system which is capable to control appliances (lighting, heating, air conditioning. TVs, etc) to provide adequate energy saving services, maximizes the inhabitants’ comfort and ensures good energy economic policy. ICA-BEMS uses a hybrid of both energy-saving approaches (active and passive) to take their advantages. ICA-BEMS uses Smart-CAM (Smart Context-Awareness Management) to correctly infer the environmental stateAnd obtain the appropriate information about the current situations. Smart-CAM detects builds and treats the situations in the smart building, to provide a complete context-awareness. This context-awareness have used by ICA-BEMS in energy efficiency reasoning to automatically perform the adequate actions and services to increase the inhabitant comfort and reduce the energy consumption. Our ICA-BEMS has been modularized to support a minimal intrusiveness of the interaction with the user. The system is able to detect energy waste context and decides adequate energy-saving services. It supports also, flexibility, scalability, extensibility, and interoperability. ICA-BEMS architecture is divided into six primary modules: Data Aggregate Middleware, Smart Context-awareness Management, Energy Efficiency Reasoning Engine, smart building ontology, building database, and User Interface. Each module has roles and it exchanges data with other modules to achieve the ICABEMS goals. ICA-BEMS Architecture is shown in Fig. 1. 3.1. Data aggregation middleware Data aggregation Middleware (DAM) is ICA-BEMS model. The DAM plays role of data-bus between ICA-BEMS system and smart building. This latter receives data from various types of appliances and sensors, such as temperature sensors, Lights, TVs and

etc. Furthermore, is responsible for modifying the environmental conditions by sends a command to actuator infrastructures such as the switch or any existing services in the smart building. The DAM model cooperates with two other ICA-BEMS models: The SmartCAM and EERE. The new aggregated data from the smart building will be sent to Smart-CAM model for treating through input-sub model. And the reasoning result produced by the ERRE will be sent to the environment through the DAM output-sub model. 3.2. Smart building ontology Ontology is one of the most powerful tools for representing a domain of knowledge. In this section, we outline some details from our ontological knowledge model called Onto-SB (Smart Building Ontology). Onto-SB is proposed to provide a structural framework for organizing smart building data. It includes machineinterpretable definitions of basic concepts of the smart building domain and relations among. Open-SB includes more than 200 concepts, such as human, environment, services, devices, places, context-awareness and others.A formal context model based on ontology can play a vital role in facilitating reasoning by formally representing domain knowledge. The structured relationships between concepts in Onto-SB are the cornerstone for enabling the reasoning.We have used the Semantic Web Rule Language (SWRL), where rules are applied for different purposes. We have used OWL (Web Ontology Language) to represent the concepts, properties, and relationships of our Onto-SB ontology.The names of the concepts and relations taken from the aforementioned ontologies are represented in the form of hyperlinks, referring to the OWL definition of the concepts. In the following sections, we briefly introduce some Onto-SB concepts. Since the ontology concepts are repre-

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sented in Description Logic language (DL), we first briefly explain the notations used in the following sections:

and relation with other concepts like profile concept. The notation bellow presents Human concept notation. Human  Onto − SB.Actor.Individual ∩

• The expression ’Name1.Name2’ and ’Name1  Name2’ means that the ”Name1” is a subclass of ”Name2”. • The term ’Onto-SB’ represents the name of our Ontology OntoSB(smart building ontology). • The symbol ’∩’ denotes’And ’. • The expression ’= Nb Name(xsd:type)’ represents the data-typeproperties of class. The number ’Nb’ represents the minimum number of data-types-properties, ’Name’ represents the name of property and ’(xsd: type)’ denotes the type of property. • The full expression ’∃ R: name’ indicates that all individuals have at least one relationship with the concept ’name’ via objectproperty ’R’. • The ’.’ denotes the end.

3.2.1. Building concept This concept is seen as the start point of all other concepts. Building concepts contain properties to describe smart building. Such as: ’Building ID’, ’Building Location’, ’Building Surface Size’, ’Physical Properties’,’ Thermal Properties’ and others. There are many object-properties which represent relationships of building concepts with other concepts. Such as the relations has-Profile, has-Energy-Sources, has-Places, Own-by, and others. Therefore, each relation has a “Range” which represents the second part of the relation. For example, the relation has-Profile has range called building-profile concept. The notations down below describe the building concepts, properties, and relationships.

Building  Onto − SB : owl.Thing ∩ = 1BuildingSurfaceSize(xsd : float) ∩ = 1 PhysicalProperties ∩ = 1ChemicalProperties ∩ = 1 ThermalProperties ∩ = 1MechanicalProperties ∩ HasLocation : Onto − SB.Location ∩ HasProfile : Onto − SB.BuildingProfile ∩ (1)

HasEnergySource : Onto − SB.EnergySource ∩ HasEnvironmentParamaters : Onto − SB.EnvironmentParamaters ∩

∃ ∃ ∃ ∃ ∃

= 1 Gender(xsd : String) ∩

(2)

= 1 Hieght(xsd : flat) ∩ = 1 Weight(xsd : float) ∩

∃ HasProfile : Onto − SB.Profile.HumanProfile ∩ ∃ HasFather : Onto − SB.Actor.Individual.Human ∩ ∃ HasMather : Onto − SB.Actor.Individual.Human 3.2.3. Profile concept This concept is used to provide an explicit representation of many things in the smart building in structured information model called Profile. Many concepts have profile Like Building, Human, Service, Site, Policy etc. Profile concept is divided into many subclasses to characterize each concept in a particular class like HumanProfile for Humans, BuildingProfile for Building etc. As an example, we present the human profile. This concept is very important and is used for many purposes like maximizing inhabitant comfort, care out about human conditions, satisfy human needs etc. Each HumanProfle has relations like activities, hasBehaviors, hasAbility, hasInterest, hashealthStat, hasRole etc. These relations have relationships with other concepts to provide a structural representation of a human profile to enable reasoning process for many purposes. The human profile concept is represented in notation down below:

= 1 ProfileID(xsd : String) ∩

= 1 RoomNumber(xsd : int ) ∩ = FloorNumber(xsd : int ) ∩

HasPlace : Onto − SB.Place ∩

= 1 Birthday(xsd : Date) ∩ = 1 Age(xsd : int ) ∩

HumanProfile  Onto − SB.Owl.Profile ∩

= 1 BuildingID(xsd : int ) ∩

∃ ∃ ∃ ∃ ∃

= 1 Name(xsd : String) ∩ = 1 LastName(xsd : String) ∩

HasEvent : Onto − SB.Event ∩ HasRequirement : Onto − SB.Requirement ∩ HasPhysicalObject : Onto − SB.PhysicakObject ∩ HasContext − Awareness : Onto − SB.Context − Awareness ∩ Ownby : Onto − SB.Actor.Human.

3.2.2. Actor concept This concept represents the inhabitant live in the smart building and any kind of actor which may exist in building and its environment. It is divided into two subclasses Group and Individual. The Group Concept includes family, friends, Brothers etc. It characterizes a group of persons. The Individual concept is spilled into two categories: Human (Father, Sister, Grandmothers etc.) and Nonhuman (pet, Robot). Each Actor has properties (Name, Age and etc.)

∃ ∃ ∃ ∃ ∃ ∃ ∃ ∃ ∃ ∃ ∃ ∃ ∃ ∃ ∃

HasRole : Onto − SB.Role ∩ HasAbility : Onto − SB.Ability ∩ HasSkills : Onto − SB.Skills ∩ HasState : Onto − SB.HumanState ∩ HasActivity : Onto − SB.Activity ∩ HasBehavior : Onto − SB.Behavior ∩ OwnPhysicalThings : Onto − SB.PhysicalThings ∩

(3)

HasPropety : Onto − SB.propety ∩ HasContant : Onto − SB.Contact ∩ OwnPlace : Onto − SB.place ∩ HasIdentity : Onto − SB.Identity ∩ HasInterest : Onto − SB.Interest ∩ HasCalender : Onto − SB.Calander ∩ HasHealthStat : Onto.SB.State.HealthState ∩ HasPreferences : Onto − SB.Preferences

3.2.4. Building things This concept includes a taxonomy of all components in the smart building. Building things are divided into two categories: controllable things and Uncontrollable thing. Uncontrollable things include furniture and architecture things in the building. Controllable things include Appliances and devices like sensors, actuators, network device, media device, Light etc. Each physical thing is located in a Place in building and it provides services. There are many properties to characterize it like ID, name, type, and proto-

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col. Each class has its own specific properties. The notation below presents the Appliance class as an example: Appliance  Onto − SB.PhysicalThing.ControlableThing ∩ = 1 ApplianceID(xsd : int ) ∩ = 1 ApplianceType(xsd : String) ∩ = 1 ApplianceName(xsd : String) ∩ = 1 ApplianceValue(sxd : float) ∩

∃ ∃ ∃ ∃ ∃ ∃

LocateInPlace : Onto − SB.Place ∩

(4)

ProvideServices : Onto − SB.Servce ∩ HasAction : Onto − SB.Action ∩ HasSofteware : Onto − SB.Softeware ∩ HasNetworkProtocol : Onto − SB : Protocol ∩ OwnBy : Onto − SB.Actor.Individual.Human

3.2.5. Energy source concept This concept is one of the most important concepts in onto-SB. It represents the different energy sources in the smart building. It is divided into two categories: Nonrenewable and Renewable. The Nonrenewable one includes grid energy, coal, oil, nuclear etc. The Renewable once includes solar, water, wind etc. Each energy source has a type (primary or secondary), provider and properties. EnergySource  Onto − SB.Source ∩

∃ hastype : Onto − SB.SourceType ∩ ∃ hasProvider : Onto − SB.Provider ∩ ∃ hasPropeties : Onto − SB.SourcePropeties

(5)

Nonrenewable  Onto − SB.EnergySource.

(6)

Renewable  Onto − SB.EnergySource.

(7)

There are many other concepts in the Onto-SB ontology, Fig. 2 presents Onto-SB classes hierarchy. 3.3. Smart building database The Building-Database is used to save information from the physical environment of the smart building such as devices stats, climate parameters or any events happening inside the smart building. It provides various ways for accessing to the data and history. It is used to automatically provide services in case of the repetition of the same situation. 3.4. User interface module The user interface module is the interface which is used to interact with users, sends them notifications and gathers feedbacks and commands from them. This interface can be the web-based application or smartphone application. 3.5. Smart context-awareness management module Smart-CAM (Smart Context-Awareness Management) Module translates building data into contextual information. It helps to make understanding the environment and situations inside the smart building easier. The big challenge in the BEMS system is how to understand the environment and how to build a correct context model. To achieve this goal, we must take into consideration all the factors that affect changing the understanding of context. We propose our contribution called Smart-CAM. This module provides context-awareness about what happening in the smart building. This context-awareness is useful for Energy Efficiency Reasoning techniques to provide best and adequate services at the right time and the right places. The primary goal is to save energy and maximize the inhabitance comfort. The Smart-CAM is responsible for

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management of the context life-cycle in our ICA-BEMS system. Our context life cycle consists of five phases. The first one is ”context acquisition”. Contexts need to be acquired from various sources. The sources could be physical (sensors, appliances, and etc) or virtual (virtual sensors, software, cloud services etc). The second one is ”context modeling”. The collected data needs to be modeled and represented according to a meaningful manner. In this section, we will use the ontology-based technique for modeling our contexts. The third one is ”Temporal context”. In this phase, we will make pre-processing of the modeled contexts. Pre-processing includes cleaning data, completing the messed data, choosing the best algorithm for each context and other operations. This step will improve the reasoning phase and lead to get the better result from reasoning. The fourth one is ”context reasoning”. Modeled data needs to be processed to derive new information and construct high-level context information from low-level contextual data. Finally, we find ”context dissemination”. Both high-level and low-level context need to be distributed to the consumers who are interested in. The consumers may be energy saving reasoning engine, user interface or any extern services. Fig. 3 presents the Smart-CAM working mechanism. • Our ICA-BEMS context life-cycle: acquisition → construction→ temporal treating→ reasoning → dissemination →. . . (reconnecting to the first phase) In this paper, we will present our contribution to modeling smart building to provide context-awareness. In the following list we will present the following adaption of context concepts: • Context definition: One of the most well-known context definitions is the Dey [46] context definition. He considers the context as any information that can be used to characterize the situation of an entity. We adopt this definition to our system as: ’ the context is a structured information model. That information related to each other logically in order to characterize a ”Thing” from multi-dimensions. A ’Thing’ is considered as ”situation”, ’person’, ’event’, ’place’, ’Object’, ’software’ or anything in smart building or its environment’. • Context types: in our ICA-BEMS system, we will use more than 226 context type. Including the most 24 context type used that presented by Perera [47]. Each context type in our system is considerate as ontology class like Time, Location, human Activity, energy source, appliances, sensor, event, etc. • Context attribute: the context attribute is considered as ’datatype-properties’ of the ontology. This element is used to set values for context type (ontology class). The data-type property is variably associated with a context type (class). It has rung and domain. The range represents the logical structural type of the data-type property like Int, String, Boolean, etc. The domain of the data-type represents wish class is associated with it. • Context schemes: It is the formula used by the ICA-BEMS to describe a situation or event in our system. We consider the context scheme as the list of contexts types. This list is infatuated with context level respecting the following format: – context-schemes-Name(Time, [Liste of Contexts-Types], [Relation with other contexts], Next-Context, Context Level, Quality-Of-Context) • Context model: an ensemble of contexts schemes related to each other constructs the context model. This model is the product of the Smart Context-Awareness Management Module. It provides a context-aware to ICA-BEMS system and provides the best understanding of the smart building. • QoC (Quality of context): In our system, we take into consideration the existing parameters to evaluate contexts including new parameters to adopt the QoC with smart building and sav-

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Fig. 2. The texonomey of Onto-SB cencepts.

ing energy issues. We used ’9’ properties to evaluate QoC which are: Energy consumption level, inhabitancefeedback, ranking history, data validity, data precision, Data up-to-datedness, quality of the physical sensor, quality of the context data and quality of the delivery process. 3.5.1. Context acquisition sub-module The role of context acquisition sub-module is collecting data from smart building components. These components can be sensors

(Humidity-Sensor, Temperature-Sensor etc.), devices (TV, Light etc.) or any smart thing is building that can communicate according to standard communication protocols (Z-Wave, ZigBee, Wi-Fi, Thread, Bluetooth etc.). We used a number of configurations to acquire data from the smart building. Each configuration has its own pros and cons [47]. We use a combination of four acquisition techniques (Responsibility-based technique, frequency-based technique, context-source based technique, and acquisition process based technique). Each configuration has its advantage and

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Fig. 3. Smart Context-Awareness Management working mechanism.

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can be used in a particular situation. When we use a combination of many approaches, we benefit from the advantage of each approach. Every piece of data collected from the building by means of sensors or devices is a piece of information that is separate from the other and does not cross enough to describe and understand a situation in the smart building. Those data will be saved in the smart building database, used to update smart building ontology state, and sent to context-construction sub-module. The data acquired by Context-Acquisition-Sub-Module is structured in the following format:

In the end, we present an extract dataset from the database table of livingroom data in Fig. 4. 3.5.2. Context-awareness constructor sub-module This module is responsible for detecting and modeling smart building contexts. It uses the result from the previous step (Context acquisition sub-module) to build the smart building contextmodel. This model contains two types of sub-models: static (do not change over time) and dynamic (can be changed over time). Both types of models are used to construct one complete smart building context model. There are many context modeling techniques to build contextual information model. Each technique has pros and Cons [47]. The key-value is a method that represents contextual information as key-value pairs in different formats such as Text files and binary files. Markup Scheme modeling (Tagged Encod-

ing) is another technique. It models data using tags. Therefore, context is stored within tags. The object-based or object-oriented technique is used to model data using class hierarchies and relationships. Object-oriented paradigm promotes encapsulation and re-usability. However, these techniques suffer from many problems like absence of structure and schema to modeling contexts, no standard processing tools are available, difficult to manage model when many levels of information are involved, Interoperability among different implementation is difficult and Lack of validation and others cons. In this paper, we will use the ontology-based modeling technique. Our choice is based on the amazing feature that ontology can provide. In addition, it can solve the problems of the previous methods mentioned above. It also supports semantic reasoning, allows more expressive representation of contexts, allows strong validation, the application is independent and allows sharing, strong

support by standardizations, fairly sophisticated tools available and other pros. This step enables the next step to perform reasoning using dataset and smart building context model. Our Contextawareness constructor sub-module typically fellows five steps to perform his task: 1 Context modeling process: This phase concerns detecting and modeling existing situations in the smart building. This phase

enables the system to understand its environment and performs reasoning to attend his goals. The result of the last step will be produced and be defined in terms of classes, attributes, and relationships. The process starts by detecting running appliances in each particular place. Then, we build for each active device its own context (this step called dividing contexts). This phase creates kind of some relationships between appliances and between other things in building like relations of near-to, placedin, connected-to. This step leads to create a number of contexts in the smart building to the model various situation in the building. We follow this mechanism of modeling because we are focused to reduce energy consumption. That’s why we follow this process to check if the running appliances are important or not, to turn them off to reduce energy. This step produces Context-modelingschema. This schema is structured to represent the situation in the smart building as shown in the following format:

• Assign state to contexts: We assign for each context schemes their own property state. This attribute helps the system to decide which context will be taken into consideration to be processed in reasoning. We used five types of context stats: Active, Suspended, Resumed, Expired and Terminated. • Assign QoC: Quality of Context (QoC) is the information used to describe the quality of information. This quality varies from contexts to other based on many factors. We used the Qoc in our system to select the best contextual information. We use ’9’ properties to characterize the QoC are Energy consumption level, Inhabitant feedback, Number of occurrences in history, Data validity, Data precision, Quality of physical sensor, Data up-todatedness, Quality of the context data and Quality of the delivery process. • Assign context level: The context degree level ’0’ is primitive information gathered from various sources of information in the

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Fig. 4. An extract dataset from our database table of livingroom data.

Building. Even information that was entered manually by the user and not fully processed is considered information of level ’0’. The context level degree ’1’ is knowledge derived from information level ’0’. The general rule for calculating the information level is:

• Organize Context According To The Model: The existing smart building model contains from many ontology individuals related with each other to represent the smart building. This model contains all the existing things in building, their properties and relationships with each other. The new contexts are used to update existing contexts in smart building model or linked new contexts models to reach the knowledge. For example, when the system detects new context like watching TV, it models this context and links it with the individual that has relation with like individual of place (Livingroom), the participant actor in contexts etc. It means the new context information needs to be merged and added to the existing context information repository. In the end, we display an example of modeling Climate Condition. Fig. 5 shows the result of modeling climate situation. Context schema of climate condition includes concepts that represent a knowledge designed to describe the state of the climate in the living room. This situation includes the status of all appliances and things that affect the climatic conditions such as air-condition, fans, the case of windows and doors open or closed and the same for sensors data concerning climatic factors such as temperature and humidity. In addition power outlets sensors to measure the consumption of different devices, available energy sources and others concepts belong to the same situation. The situation also contains information that determines the presence of the human being in the same place using moving sensors and RFID-TAG sensors. Fig. 6 presents a graphic diagram of climate condition context-schema. The following context-schema represents this situation:

3.5.3. Temporal context sub-module This phase of context life-cycle is important to increase the efficiency of reasoning phase. This step concerns to make preprocessing on context schemas of modeling step. The temporal

context features are represented like: cleaning data by removing Non-useful information, merged the same information that collected from different information sources, select the important

contexts based on the quality of contexts, completing the missed data to provide all necessary information to describe each situation and keeping contexts in temporal phase when contexts have not yet matured and are not yet complete (state of contexts is noted Active). Temporal Context Sub-Module playing the role of context buffer. This option is very important to organize the contexts in the system and keep the unprocessed context in a buffer until they get processed. For this feature the temporal context using many types of Scheduling Algorithms, depending on its configuration (First-Come-First-Serve Scheduling, Shortest-Job-First Scheduling, Priority Scheduling, Round Robin Scheduling etc). 3.5.4. Context reasoning sub-module Context reasoning or context inference can be defined as a phase of deducing new contextual knowledge based on available contexts to provide a better understanding of the environment in the smart building. There are many reasoning techniques which can be used to deduce new knowledge like supervised learning, unsupervised learning, fussy logic, and others. In [47], authors present statistic of the most used technique for reasoning in context-awareness approach. In this paper, we will use the most used technique which is technique rules. It is used by 54% of total using techniques. In this step, we use the SWRL (Semantic Web Rule Language) and SQWRL (Semantic Query-Enhanced Web Rule Language) to explore the ontology context model produced by the previous step to deduce

new knowledge using existing context information. For example, in Fig. 5, the climate condition context schema describes the environmental condition in the living room. This information includes

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Fig. 5. Results achieved after modeling the climatic condition situation.

the temperature of the room (inside and outside), humidity, the state of the windows in the room (Open or closed), air-conditioner, Energy consumed by the air conditioner and other contextual information. In this example, there’s no information about the human profile like health state. If the person is sick with a cold, the miss of this information effect negatively on the decision of the temper-

ature chosen to fix this context. Many other pieces of knowledge can be deduced like calculate the season based on weather information, identify the existing persons based on RFID sensor data, count the energy consumption of different appliances and contexts in each place and appliances that consume energy in building etc. This information is used to enrich the knowledge in a context model

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Fig. 6. Sequence diagram of ICA-BEMS working mechanism.

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to reinforce the reasoning phase to minimize energy consumption and maximize inhabitance comfort. In this section, we will present some rules used by our context reasoning sub-module to deduce new knowledge: • Detecting watching Tv Activity:

3.6. Energy efficiency reasoning engine EERE (Energy Efficiency Reasoning Engine) is a part of the system that generates conclusions and decisions from available knowledge about the smart building. It plays a significant role in the implementation of our ICA-BEMS. The EERE defines which service to provide

• Calculate the energy consumption of each device:

• Calculate the available green energy in the smart building:

• Determine place where there is no respect for the energy consumption policy:

3.5.5. Context dissemination sub-module Also called ”context sharing” is the last step in Smart-CAM module. After inferring process, the smart building context-awareness model becomes ready to share with other modules. We use two contexts sharing techniques: query and subscriptions. The query method uses SQWRL query to consult the ontology or uses SQL to consult the database. Second, any services or modules that have subscriptions within context dissemination module will receive the new information automatically. In the end, we present some SQWRL rules for consulting context-awareness smart building model: • Consulting the temperature in the each room and emerging values from different data sources:

• Consulting the available energy production from photovoltaic sources:

for each context that has been produced and inferred by SmartCAM. It detects energy waste causes and contexts by using rules. It provides energy saving measures to eliminate energy waste. Our EERE uses SWRL rules. Those rules are represented as conditional logic. Rule sets can be managed and applied separately to other functionality. Each rule binds a conjunction of predicate clauses to a list of executable actions. At runtime, the rule engine matches productions against facts and executes the associated action list for each match. EERE changes environment stats and controls Appliances. The first step of the EERE is to arrange situations in the building. This is a descending order of the situation from the most important contexts to the least important one. The order is based

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on the Quality of Contexts characteristics in each context. The benefit of the arrangement is to deal with the most important and most wasteful situations of energy. If the system leaves a situation of the most important and the most lost contexts of energy in the last order of processing, will negatively affect the energy consumption (The cost will be high). That’s why the system processes the context in this way in order to minimize the wasted consumption. The rule that arranges the contexts is shown below:

After arranging the contexts, the EERE uses incorporate multiple energy efficiency strategies together to complement each other’s’ strengths and mitigate their weaknesses. The EERE apply energy efficiency rules from both approaches passive and active based on context-awareness knowledge. The list below presents some of EERE rules: 1 Rules that Provide feedback to user through sending adequate notification message to increase inhabitant aware about energy consumption: • The rule that alerts the user when he forgets to turn lights off:

• The rule that alerts the user when household consumption of electric power is greater than the value specified in the building energy consumption policy:

• Rule to notify the user when he left the devices in standby mode:

• The rule that alerts the user about behaviors and activities consuming the largest proportion of electric power:

• The rule that alerts the user about where is the most energy consuming at building:

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• Rule to notify user to stop opening fridge all the time (energy wast Behaviors):

• Rule to determine and deal with the cause responsible for the direct waste of energy:  This rule extinguishes the light in case of there is no person in a room:

 A rule that detects and extinguishes operating devices that are useless and unused by the user:

 A rule that turns the heater off when the temperature greats then ’25’:

• Rules that determines which factors influence the energy consumption at any time: • This rule opens blinds and windows to allow external light to enter the room without the need to use lighting appliances:

• Rule to exploit the building architecture to change the temperature without needing Climate Appliances, to reduce energy consumption by opening doors and windows:

• Rule that maximize the inhabitants comfort using automatic services execution feature: • The rule that automatically turns on the lights when the user enters a particular location to increase user comfort:

• This rule changes the temperature of cooling devices based on user profile condition and his preferences to maximize the comfort and care about the health condition of inhabitant:

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• The Rule runs the needed devices automatically when the time of user activity comes, to increase user comfort:

• The rule extinguishes all the other useless devices when bedtime comes: • The rule that learns the user preferences about likely temperature in the bedroom:

4. The solution scheduling ICA-BEMS system uses an intelligent inferring mechanism. This last allows the system to detect energy waste contexts, and produce an adequate decision for each context. Those decisions ensure comfort and reduce energy consumption for the inhabitant. In this section, we will present the general process of ICA-BEMS working mechanism. Fig. 6 display ICA-BEMS scheduling mechanism. The smart building is one of the most dynamic environments. The parameters and stats of appliances change frequently. The ICABEMS mechanism process starts due to a happening change in the smart building (step 1 and 2 in Fig. 6). Once the sensors and devices detect the change; they send data to the Data Aggregation module (step 3). When Data Aggregation module receives and collects data from appliances infrastructure thought input-bus, it sends data to the Smart-CAM module for treating (step 4). Smart-CAM receives data throughout context acquisition submodule. This last saves the new information in the smart building database, and updates the smart building ontology to make it current with the real-time stat of the smart building (step 5, 6). Following this step, the context-awareness constructor sub-module consults the database and ontology to detect and model existent contexts in the smart building in real-time (step 7, 8, 9). The context in the smart building is an ensemble of relationships between relevant information, condition, actors, events, concepts, and other things which characterize a particular situation in the smart building. This context can be used by the computer system to understand the real environment in the smart building. After modeling, these contexts will be inserted into a list of contexts which represents all happening situations inside the smart building. Each context will be taken alone for passing to the next steps. Before starting with the reasoning process, each context passes the step pre-preceding performed by Temporal Context sub-module. Pre-preceding includes many operations such as: removing non-useful information, merging the same information, and others (Step 10). Once the pre-proceeding finished (step 11); the Context Reasoning sub-module produce new contextual knowledge based on available contexts. This step produces a high knowledge level. It makes context treating easier and efficient to provide adequate decision (step 12). Subsequently, the Context Dissemination Sub-Module save the reasoning result into the database, and update the smart building ontology (step 13, 14). Each context will pass the checking step (Step 15). There are three types of checking results. First, if the context is already examined and treated, the system will directly move to the step 37. The system is previously trained to treat this context. The ICA-BEMS consults the database and ontology to provide direct decisions for this context (step 38-42). The data aggregation module sends the decision to the environment. In the end, actuator and appliances infrastruc-

ture receive commands and change their state to reduce the energy consumption (step 28, 29). Second, when checking phase detects unimportant contexts based on the quality of context measurements, the system will skip this context, and stop their treatment (step 43). Third, when the context is different (was not treated before); the system pass to step (16). Afterward, each context passes to EERE module. EERE module detects energy waste contexts, produces adequate decision, and services to eliminate energy waste case (step 17, 18). The EERE reasoning results return back to Smart-CAM model for checking (Step 19). The checking results are divided into two types. First, when the result is positive, the mechanism process passes to the tagging phase (step 23). In this phase, the system gathers for each context its own provided services, and save this information in the database for future proposes (step 24, 25). This phase supplies the functionality of the learning. When the ICA-BEMS detects the same context, the provided information may be utilized later. After tagging, the Smart-CAM module sends decisions, and commands to the Data Aggregation module (step 27). Once this last receives commands, it will distribute them to coarsen actuators and appliances (step 28). The infrastructure devices apply those commands to eliminate energy waste causes, and satisfy users need (step 29). Second, when the result of checking is negative, the scheduling passes to step 30. In this case, the system consults the user to propose suggestions. The Smart-CAM sends the contexts and results after reasoning to the user interface. The user interface receives the data, and starts consultation. After consultation, the system backs to step (20). The user suggestions help system to treat the context quickly and perfectly. This step allows the system to learn the user performances. The system follows the steps until the last step (29) to apply reasoning results in a smart building to reduce energy consumption, and satisfy user needs. 5. ICA-BEMS implementation First, we start by the implementation of our smart building ontology. To have a machine-readable ontology, we use the Protégé5 ontology editor [48]. This editor allows translating the ontology in different languages like the OWL [49]. We create all our hierarchical classes, and we add for each concept its properties and relationships as shown in Fig. 7. The Ontology rules are an important part of our ICA-BEMS system. They define the way to exploit our smart building ontology. We use The SWRLTab which is a Protégé plugin that provides a development environment for working with SWRL rules and SQWRL queries. Fig. 8 presents the implementation of our rules in SWRL TAB on Protege 5. After finishing the implementation of our smart building ontology, we execute the verification process using protege debugger. The protege debugger uses many reasoners to verify the ontology like

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Fig. 7. The implementation of our smart building ontology in Protege 5.

Fig. 8. The implementation of rules in SWRL TAB on Protege 5.

Pellet, Fact++, RacerPro, and KAON2. The verification of our OntoSB ontology presents positive result. Our Onto-SB is consistent and coherent. We used protege 5 to export the ontology to OWL file called ’Onto-SB.owl’. This file contains the full description of our ontology and our rules. This file will be imported into the programming phase of our ICA-BEMS system. We used the JAVA programing language to develop the modules of our ICA-BEMS. We used Eclipse [50] Development Environment (IDE) editor. Protege API is used to manipulate our ontology using JAVA. To test our ICA-BEMS system we develop a smart building simulator to test the functionality of our system. We did not use the existing simulators [51–54] because of they safer from many lacks, for example, They did not support the ontology-based systems and provide the ability to run the SWRL rules and show the result on simulation. They did not simulate behaviors and activities for multi inhabitants in the same simulation. They missed an interface to control the simulator through extern reasoning engine and through other systems. Most of them did not support the energy consumption concepts like tracking energy consumption and simulation of green energy production. They did not cover many types of appliances. They are not based on structural information model that supports the context-awareness based approach, to provide contextual information about the situations in the smart building. They did not support the context-awareness based systems.

The miss of a simulator which includes those features makes us unable to test our ICA-BEMS capabilities. So we develop a new smart building simulator called Open-SBS (Open Source Smart Building Simulator) using JAVA language. Open-SBS handles all the previous limitations. In addition, it offers many advantages such as designing the virtual smart building, creating things in a smart building for example appliances, inhabitants, infrastructure, and others. Open-SBS provide the ability to configure the environmental parameters and many other parameters in the smart building, creating scenarios and save virtual smart building model and scenarios in a file. This feature allows importing already created models without repeating all the creation process. Furthermore, it running the scenarios and generates various kinds of results like energy consumption tracking. Open-SBS simulates the human behaviors, activities, and interaction of inhabitants with a different appliance. Open-SBS measures the energy consumption and permits to control appliances and everything in virtual smart building through ICA-BEMS system. To evaluate our ICA-BEMS, we use the Open-SBS simulator. First, we create a smart building model as an example of the real building in Ouargla city. Second, we make scenarios of typical daily life. Third, we start the simulation by executing the scenarios in four cases: in a typical situation, under control passive methods, using active methods and under the control of our ICA-BEMS system.

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Fig. 9. The list of appliances and the energy consumption in each room in the smart building model.

Fig. 10. The virtual smart building in the Open-SBS simulator after creation.

Finally, we present the energy consumption tracking result of the four cases of simulation. 5.1. Creating virtual smart building The first step is creating the virtual smart building model in the Open-SBS simulator. This building contains from seven room, four humans, and appliances like air-conditioning, heating, refrigerator, TV, lights, camera, and others. The model includes also the description of the human profile for each member of the family. This model also includes specific environment parameters of Ouargla city in Algeria country. Ouargla is Sahara place. It is so hot in summer and cold in winter. The energy consumption is so high in a residential building in Ouargla. Fig. 9 presents the distribution of the appliances in each room and energy consumption property of each device. The model is created based on our smart building ontology. It includes many individuals that generated from many classes like time, event, Activities, and others. Each individual has properties and relationships with other individuals to build the smart building model. After we finished the modeling of a smart building model, we create this smart building in Open-SBS simulator. We import the building map. Open-SBS provides the ability to import maps to simulate many types of building. Then we create all appliances

and components of the smart building in the simulator. Each appliance is characterized by an ID, energy consumption, state, location, and other parameters. Then, we configure some other parameters like energy sources and environmental parameters such as temperature, illumination, humidity, and others. These parameters are important to simulate the real building in Ouargla city. Afterward, we create the actors represented by human, pets, and robots. We defined for each actor his properties and profile. The human profile provides an implicit description of human and includes many concepts needed to simulate the comportment of inhabitant in the simulator like activities, behaviors, interest, ability, health stat and other. Each class in ontology can be created as individual in Open-SBS simulator and provide the ability to set properties and relationships with other existing individuals in the smart building model. The Open-SBS simulator makes this task easily by provide the user interface to create things and configure the smart building ontology model automatically. Fig. 10 presents the virtual smart building in the Open-SBS simulator after creations. 5.2. Creating scenarios in Open-SBS simulator Open-SBS simulator allows us to create many scenarios. Each scenario is characterized by some properties: duration, places, human activities, environmental parameters, and others. The sce-

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Fig. 11. An extract example from our scinarios.

Fig. 12. The interface of the creation scenarios in Open-SBS.

nario contains from a list of events. Each event affects the smart building during the simulation. An event can be for example: changing appliances stats, movement of actors in the building, the interaction of actors with appliances etc. Open-SBS integrates two types of energy sources: grid source and green energy production. To evaluate our ICA-BEMS system, we create scenarios for a typical daily life (24 hours) in Ouargla city. The table in Fig. 11 presents an extract from our scenario. Fig. 12 presents the interface of the creation scenarios in Open-SBS simulator. 5.3. Simulation and result After creating the virtual smart building and scenarios using Open-SBS, we start the simulation of the scenarios. The simulation takes a long time to end because the scenarios duration is

24 hour. Open-SBS provides the ability to minimize the time of simulation by applying time reducing techniques without effect in the simulation result. The time in Open-SBS reduced from 24 hours to 24 minutes. When the simulation starts the actors start moving in the smart building, appliances change their stats. The comportment of the smart building simulated using the scenarios is shown in Fig. 11. In the top of Fig. 13, Open-SBS shows the tracking of the building energy consumption during the simulation. The center of Fig. 13 includes a workspace environment of simulation which contains from the building, application, inhabitant and other things. In the button, Open-SBS presents in a table: the energy waste situations detected by ICA-BEMS, the rule ID used to deal with each situation and the action by ICA-BEMS to fix it in order to reduce energy consumption and maximize inhabitant comfort. In the end, Open-SBS presents the simulation result. The result con-

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Fig. 13. The simulation interface of the scenarios in Open-SBS.

Fig. 14. The tracking of the energy consumption of the smart building scenario number 1.

cerns the energy consumption stats. These stats are represented in sharps. We integrated ICA-BEMS system inside the Open-SBS simulator. Open-SBS offers the possibility to execute the simulation of scenarios in four cases. Figs. 14 present the energy consumption tracking of simulation of the first scenario. The red line shows the level of energy consumption of the appliance in the typical case (without applying any controlling). The yellow line represents the energy consumption tracking under control passive methods. The blue line represents the energy consumption tracking under con-

trol active methods. Finally, the green line represents the tracking of energy consumption under control our ICA-BEMS system. In the scenario of Fig. 14, the total energy consumption, in a typical case, was 10524 kW/day. When we use the passive methods the energy consumption is reduced only to 8385 kW/day. The energy consumption is reduced by only 20.32 % of total consumption. In the case of the active methods, the total energy consumption was 7207 kW/day. The energy consumption has been reduced by about 31.51 %. But under control of our ICA-BEMS that used hybrid technique

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Fig. 15. The energy consumption tracking states of seven different scenarions.

based-on Smart-CAM (smart context-awareness management), the energy consumption has been reduced to 6269 kW/day. This means 40.43 % is from total energy consumption. In the end, we present the energy consumption tracking states of seven different scenarios (Fig. 15). We got a positive result using our ICA-BEMS system. The reduction average of energy consumption in seven scenarios is about 40%. 6. Conclusions The main goal of the current study was to propose a new energy efficiency approach in a smart building. To attend our goal, we have treated two challenges: first, how modeling the smart building and its environment to provide context-awareness. Second, how to make a system detect and understanding contexts easily, especially in complex situations, and how to exploit this awareness to reduce energy consumption and maximize the user comfort. To attend our goal, we proposed in this paper an intelligent Context-Awareness building energy management system (ICA-BEMS). The ICA-BEMS consists of six modules: Data Aggregation Module, Smart-CAM (smart context-awareness management), EERE (energy efficiency reasoning engine), User interface, Database, and Smart building Ontology. ICA-BEMS is able to detect and understand the various contexts in the smart building. It is also able to identify the particular device or behavior causing the energy waste and eliminating this waste context by providing adequate energy saving decisions. ICA-BEMS uses hybrid energy-saving techniques based on SmartCAM (smart context-awareness management). In Smart-CAM, we have provided a deeper insight into modeling smart building contexts based on smart building ontology, to organizing building knowledge into a structural framework model. Smart-CAM module consists of 5 sub-modules: context-acquisition, context-awareness constrictor, temporal context, context reasoning, and context dissemination. Each sub-module has role and collaboration between each other to provide better understating about smart building and its environment. The ICA-BEMS use EERE (energy efficiency reasoning engine) to exploit the contextual information of Smart-CAM to reduce energy consumption and maximize inhabitant comfort. To evaluate our ICA-BEMS, we have developed Open-SBS (open source smart building simulator). Open-SBS simulates the comportment and functionality of the real smart building and simulates the human behaviors, activities and the interaction with appliances

in the smart building. Open-SBS provides the ability to create various models of smart building and executing many scenarios in those models. We have created a model of a typical building in the town of Ouargla in Algeria. We test many scenarios. Each scenario has been tested four times: in typical conditions without any controlling, under control Passive approaches, under control Active Approaches and under control of our ICA-BEMS system. We got a positive result using our ICA-BEMS system. The average energy consumption in seven scenarios has been reduced about 40% from total energy consumption. This result is better than both old approaches. The average for using passive approaches is 20.56% and Active is 32.07%. As perspectives: We plan to transform the ICA-BEMS to a distributed system based on MAS (Multi-Agent System), The intelligent will be distributed to multi-agent that collaborate and coordinate with each other to attend common goals. This evolution will make the ICA-BEMS profit from the advantage of the MultiAgent Systems. We will develop a smarter reasoning engine using a hybrid artificial intelligence algorithm and also focus on developing a new 3D version of our Open-SBS simulator.

References [1] P.H. Shaikh, Nor N.B. Mohd, P. Nallagownden, I. Elamvazuthi, T. Ibrahim, Robust stochastic control model for energy and comfort management of buildings, Aust. J. Basic Appl. Sci. 7 (10) (2013) 13744. [2] International Energy Agency, World Energy Investment Outlook (WEIO): special report, June 2014. [3] U.S. Energy Information Administration, International EnergyOutlook 2013, July 2013. [4] A. De Paola, M. Ortolani, G. Lo Re, G. Anastasi, S.K. Das, Intelligent Management Systems for Energy Efficiency in Buildings, ACM Comput. Surv. 47 (1) (2014) 1–38. [5] P.H. Shaikh, N.B.M. Nor, P. Nallagownden, I. Elamvazuthi, T. Ibrahim, A review on optimized control systems for building energy and comfort management of smart sustainable buildings, Renew. Energy Sustain. Energy Rev. June 34 (2014) 409–429. [6] M. Weiss, D. Guinard, Increasing energy awareness through web-enabled power outlets., In Proc. of the 9th Int. Conf. on Mobile and Ubiquitous Multimedia. 20:1-20:10 (2010). [7] Google PowerMeter. 2011. Homepage. Available at http://www.google.com/powermeter. [8] Microsoft Hohm 2011. Homepage. Available at http://www.microsoft.com/environment/. [9] Houssem Degha Eddine, Fatima Zohra Laallam, Djamel Saba, Chaima Ayachi Omar, Maroua Seggar, Towards: Development of an Energy Monitoring System for Renewable Energy Sources in Residential Building, 2017.

H.E. Degha, F.Z. Laallam and B. Said / Sustainable Computing: Informatics and Systems 21 (2019) 212–233 [10] H. Degha, Z. Laallam, O. Chaima, et al., Implementation of an energy monitoring system based on Arduino, Artificial Intelligence and Its Applications, 2018. [11] H. Degha, Z. Laallam, D. Sab, et al., Ontology-driven Based approach for Smart home Simulation Supporting Energy Efficiency, Artificial Intelligence and Its Applications, 2018. [12] H.E. Degha, et al., Human Profile Ontology for Energy Efficiency in Smart Building, The International Conference on Pattern Analysis and Intelligent Systems (2018). [13] Djamel Saba, Brahim Berbaoui, Houssem Eddine Degha, Fatima Zohra Laallam, A generic optimization solution for hybrid energy systems based on agent coordination, AISI (2017) 527–553. [14] Djamel Saba, Fatima Zohra Laallam, Hocine Belmili, Fonbeyin Henry Abanda, Ahmed Bouraiou, Development of an ontology-based generic optimisation tool for the design of hybrid energy systems, IJCAT 55 (3) (2017) 232–243. [15] Djamel Saba, Fatima Zohra Laallam, F. Brahim Berbaoui, Henry Abanda, An energy management approach in hybrid energy system based on agent’s coordination, AISI (2016) 299–309. [16] Saba Djamel, Houssem Eddine Degha, Brahim Berbaoui, Fatima Zohra Laallam, Rachid Maouedj, Contribution to the modeling and simulation of multiagent systems for energy saving in the habitat., in: In Mathematics and Information Technology (ICMIT), 2017 International Conference on. IEEE, 2017, pp. 204–208. [17] Djamel Saba, Houssem Eddine Degha, Brahim Berbaoui, Rachid Maouedj, Development of an Ontology Based Solution for Energy Saving Through a Smart Home in the City of Adrar in Algeria, AMLTA (2018) 531–541. [18] Pulse Energy Inc 2013. Berkeley Energy Dashboard. Available athttps://us.pulseenergy.com/ UniCalBerkeley/dashboard. [19] M. Weiss, D. Guinard, Increasing energy awareness through web-enabled power outlets., In Proc. of the 9th Int. Conf. onMobile and UbiquitousMultimedia. 20:120:10 (2010). [20] Richardson and S. Ruby 2007. RESTful Web Services. [21] Poort Joost, Jorna Leenheer, Jeroen van der Ham, Cosmin Dumitru, Baywatch: Two approaches to measure the effects of blocking access to The Pirate Bay, Telecommun. Policy 38 (4) (2014) 383–392, – website [https://pdfs. semanticscholar.org/ae42/d2b460c939543b897d9c51f493047d72886e.pdf]. [22] BE3SoHo Project, 2011, Website http://www.e3soho.eu/. [23] Y. Kim, Z. Charbiwala, A. Singhania, T. Schmid, M. B. Srivastava, SPORTLIGHT:Personal Natural Resource Consumption Profiler, HotEmNets 2008, 2008. [24] J. Lu, T. Sookoor, V. Srinivasan, G. Gao, B. Holben, J. Stankovic, E. Field, K. Whitehouse, The smart thermostat: using occupancy sensors to save energy in homes, in: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (SenSys’10), 010. [25] Y. Agarwal, B. Balaji, R. Gupta, J. Lyles, M. Wei, T. Weng, Occupancy-driven energy management for smart building automation, Proc. 2nd ACM Work. Embed. Sens. Syst. energy-efficiency Build (2010) 16. [26] H. Doukas, K.D. Patlitzianas, K. Iatropoulos, J. Psarras, Intelligent building energy management system using rule sets, Build. Environ. 42 (10) (2007) 3562–3569. [27] A.-G. Paetz, E. Dtschke, W. Fichtner, Smart homes as a means to sustainable energy consumption: a study of consumer perceptions, J. Consum. Policy 35 (1) (2012) 2341. [28] L.W. Yeh, Y.C. Wang, Y.C. Tseng, iPower: An energy conservation system for intelligent buildings by wireless sensor networks, Int. J. Sensor Networks 5 (1) (2009) 110. [29] G. Corucci, F. Anastasi, Marcelloni, A WSN-based testbed for energy efficiency in buildings., In Proc. of the 16th IEEE Symp. On Computers and Commun. (ISCC11) (2011) 990993.

233

[30] Christian Reinisch, Kofler MarioJ, Félix Iglesias, Wolfgang Kastner, Thinkhome energy efficiency in future smart homes, EURASIP J. Embedded Systems 2011 1 (2011) 104617. [31] eDIANA Project, 2011, Website http://www.artemis-ediana.eu/. [32] AIM Project, 2011,Website http://www.ict-aim.eu/home.html/. [33] M.C. Mozer, March. The neural network house: An environment hat adapts to its inhabitants, In Proc. AAAI Spring Symp. Intelligent Environments (Vol. 58) (1998). [34] Adaptive House Project, 2011, Website http://www.cs.colorado.edu/ mozer/nnh/. [35] W. Gao, J.L. Guirao, B. Basavanagoud, J. Wu, Partial multi-dividing ontology learning algorithm, Inform. Sci. 467 (2018) 35–58. [36] J. Wu, X. Yu, W. Gao, Disequilibrium multi-dividing ontology learning algorithm, Commun. Stat.-Theory Methods 46 (18) (2017) 8925–8942. [37] W. Gao, M.R. Farahani, Generalization bounds and uniform bounds for multi-dividing ontology algorithms with convex ontology loss function, Comput. J. 60 (9) (2017) 1289–1299. [38] B. Thomas, D. Cook, Activity-aware energy-efficient automation of smart buildings, Energies 9 (2016) 624 [CrossRef]. [39] T.A. Nguyen, M. Aiello, Energy intelligent buildings based on user activity. A survey, Energy Build. 56 (2013) 244–257. [40] S.N. Han, G.M. Lee, N. Crespi, Semantic Context-Aware Service Composition for Building Automation System, IEEE Trans. Ind. Inform. 10 (2014) 752–761 [CrossRef]. [41] C. Kamienski, F. Borelli, G. Biondi, W. Rosa, I. Pinheiro, I. Zyrianoff, F. Pramudianto, Context-aware energy efficiency management for smart buildings., in: In Proceedings of the 2015 IEEE 2ndWorld Forum on Internet of Things (WF-IoT), Milan, Italy, 14-16 December, 2015, pp. 699–704. [42] V. Moreno, M.A. Zamora, A.F. Skarmeta, A Low-Cost Indoor Localization System for Energy Sustainability in Smart Buildings, IEEE Sens. J. 16 (2016) 3246–3262 [CrossRef]. [43] M. D’Aloia, F. Cortone, G. Cice, R. Russo, M. Rizzi, A. Longo, Improving energy efficiency in building system using a novel people localization system., in: In Proceedings of the 2016 IEEE Workshopon Environmental, Energy, and Structural Monitoring Systems (EESMS), Bari, Italy, 13-14 June, 2016, pp. 1–6. [46] G.D. Abowd, A.K. Dey, P.J. Brown, N. Davies, M. Smith, P. Steggles, Towards a better understanding of context and context-awareness, in: in Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing, ser. HUC ’99. London, UK: Springer-Verlag. [Online]. Available: http://dl.acm.org/citation.cfm?id=647985.743843, 1999, pp. 304–307. [47] C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, “Context Aware Computing for The Internet of Things: A Survey,” vol. X, no. X, pp. 1-41, 2013. [48] Mark A. Musen, Protege ontology editor, Encyclopedia of Systems Biology (2013) 1763–1765. [49] W3C Owl Working Group. OWL 2 Web Ontology Language Document Overview. (2009). [50] Eclipse, I. D. E. Eclipse Foundation. (2007). [51] T. Nguyen, T. Van Nguyen, S. Member, J.G. Kim, D. Choi, ISS: The Interactive Smart Home Simulator, 2009. [52] T. Van Nguyen, H. Nguyen, and D. Choi, Development of a Context Aware Virtual Smart Home Simulator, Computer (Long. Beach. Calif)., vol. 8, p. Proc. of ICUT’08, 2010. [53] S.H. Kim, B.J. Choi, M.S. Jung, K.S. Park, A real time network game system based on retransmission of N-based game command history for revising packet errors, Proc. - SERA 2007 Fifth ACIS Int. Conf. Softw. Eng. Res. Manag. Appl (2007) 917–923. [54] B. Bouchard, SIMAC: A 3D Open Source Smart Home Simulator, vol. 5, no. March 2016, pp. 13-32, 2010.