Intelligent context-awareness system for energy efficiency in smart building based on ontology
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 energy-related 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 efficiency has significant gains like saving money and good impact on the environment. Therefore several works have been conducted to saving energy [4], [5], [6], [7], [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 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 context-awareness 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). ICA-BEMS 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.
Section snippets
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], [10], [11], [12], [13], [14], [15], [16], [17]. They are divided into two main approaches: Active and Passive.
ICA-BEMS architecture and features
In this paper, we propose our new architecture for BMS systems called ICA-BEMS (Intelligent Context-Awareness Building Energy 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
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.
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
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
References (52)
- et al.
A review on optimized control systems for building energy and comfort management of smart sustainable buildings
Renew. Energy Sustain. Energy Rev. June
(2014) - et al.
Intelligent building energy management system using rule sets
Build. Environ.
(2007) - et al.
Partial multi-dividing ontology learning algorithm
Inform. Sci.
(2018) - et al.
Energy intelligent buildings based on user activity. A survey
Energy Build.
(2013) - et al.
Robust stochastic control model for energy and comfort management of buildings
Aust. J. Basic Appl. Sci.
(2013) - International Energy Agency, World Energy Investment Outlook (WEIO): special report, June...
- U.S. Energy Information Administration, International EnergyOutlook 2013, July...
- et al.
Intelligent Management Systems for Energy Efficiency in Buildings
ACM Comput. Surv.
(2014) - et al.
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) - Google PowerMeter. 2011. Homepage. Available at...
Towards: Development of an Energy Monitoring System for Renewable Energy Sources in Residential Building
Implementation of an energy monitoring system based on Arduino
Ontology-driven Based approach for Smart home Simulation Supporting Energy Efficiency
Human Profile Ontology for Energy Efficiency in Smart Building
The International Conference on Pattern Analysis and Intelligent Systems
A generic optimization solution for hybrid energy systems based on agent coordination
AISI
Development of an ontology-based generic optimisation tool for the design of hybrid energy systems
IJCAT
An energy management approach in hybrid energy system based on agent's coordination
AISI
Contribution to the modeling and simulation of multiagent systems for energy saving in the habitat.
Development of an Ontology Based Solution for Energy Saving Through a Smart Home in the City of Adrar in Algeria
AMLTA
Increasing energy awareness through web-enabled power outlets.
In Proc. of the 9th Int. Conf. onMobile and UbiquitousMultimedia. 20:120:10
Baywatch: Two approaches to measure the effects of blocking access to The Pirate Bay
Telecommun. Policy
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2023, Advanced Engineering InformaticsIntelligent energy management: Evolving developments, current challenges, and research directions for sustainable future
2021, Journal of Cleaner ProductionCitation Excerpt :In Prasad and Rajalakshmi (2013), a heterogeneous wireless network comprising various radios for context-aware energy management in a building was suggested. A context-aware energy management module was proposed in Degha et al. (2019). Fig. 10 illustrates the structure of the proposed module, in which various components communicate with one another to save energy and optimize occupant comfort.