Exploring Cross-Domain Data Dependencies for Smart Homes to Improve Energy Efficiency Shamaila Iram Terrence Fernando May Bassanino University of Huddersfield University of Salford University of Salford Huddersfield, United Kingdom Salford, United Kingdom Salford, United Kingdom [email protected] [email protected] [email protected] ABSTRACT well as from the government agencies in the past few years. The rapidly increasing energy consumption rate poses an alarming Over the past decade, the idea of smart homes has been conceived threat to the worldwide environmental sustainability and economic as a potential solution to counter energy crises or to at least mitigate stability. International Energy Agency’s (IEA) statistics reveal that its intensive destructive consequences in the residential building 32% of the total final energy is being consumed by the buildings sector. Smart homes have emerged as one of the applications of [1]. This percentage is even higher in non-industrial areas. In recent Internet of Things (IoT) that enabled the use of technology to years, energy efficiency and saving strategies have become a automate and customize home services with reference to users’ priority objective for energy policies due to the proliferation of preferences. However, the concept of smart homes is still not fully energy consumption and CO2 emission in the built environment. matured due to the weak handling of diverse datasets that can be According to statistics [2], globally, 40% of all primary energy is exploited to promote more adaptive, personalised, and context being consumed in and by the buildings and contributes 30% of the aware capabilities. Furthermore, instead of just deploying CO2 emissions [3]. In many developed countries, the buildings are integrated automated services in the homes, the focus should be to considered as highest energy consumption sector than bring the concerns of potential stakeholders into consideration. In transportation and industry. For instance, in 2004, building sector this paper, we have exploited the concepts of ontologies to capture has consumed 40%, 39%, and 37% of total primary energy in USA, all sorts of data (classes and their subclasses) that belong to one the UK and the European Union [2]. The fact that how people domain based on stakeholders’ requirements analysis. We have consumes energy depends on human behaviour and other social, also explored their significant associations with other datasets from economic, environmental and geographical factors. another domain. In addition, this research work provides an insight Data plays a significant role in the manipulation and exploitation of about what sorts of interdependencies exist between different smart homes applications and services. Unfortunately, tremendous datasets across different ontological models such as Smart homes amount of data that is being generated in smart homes domain is ontology model and ICT ontology model. usually not properly collected, structured and stored under their relevant domains. This eventually raise more issues in seamless CCS CONCEPTS integration, exchange and reusability of data for its adaptation and • Computer systems organization → Internet of Things; Smart manipulation to multiple services [4]. This research work discusses Homes • Data Modelling → Ontology Modelling the potential benefits of formally modelling the data for smart home services to deal with rich metadata and their semantics. This KEYWORDS research work also contributes to the existing work by Smart homes, Energy efficiency, Data ontology, Internet of Things, conceptualising the idea of extending modelling paradigm from Use case studies smart home data to modelling the tools and techniques that belong to one particular concept within ICT domain. Determining the ACM Reference format1: interrelationship between two domains i.e., smart home and ICT, S. Iram, T. Fernando, and M. Bassanino. 2017. Exploring Cross-Domain and realizing the interdependencies among multiple concepts inside Data Dependencies for Smart Homes to Improve Energy Efficiency. In Proceedings of ACM/IEEE UCC conference (UCIOT2017). ACM, New their domain could potentially facilitate seamless exchange of data York, NY, USA, 6 pages. and information to deploy significant services. DOI:.10.1145/3147234.3148096 In order to address the issues relating to data handling, data exploitation and data visualization in smart homes applications, this 1 INTRODUCTION research work is conducted with the intension of seeking answers Smart homes have gained a tremendous interest from the research to the following questions: community, homes owners, energy providers, housing agencies as UCIOT2017, Dec 2017, Austin, TX, USA S.Iram et al. Table 1: Functional demands of different stakeholders in designing and developing the Energy Efficient Smart Buildings Stakeholders Analysis Occupiers/householders Energy Providers Housing Agencies/ Landlords Government/policy makers 1. Increased comfort 1. Demand and supply 1. Increased comfort level 1. Comfort level level monitoring 2. Relative humidity rate- 2. Revenue protection 2. Tracking energy 2. Demand and supply to avoid condensation, 3. Saving energy consumption balance Dump or Mould 4. Energy usage per month or 3. Energy consumption 3. Load prediction 3. Cost estimation per year patterns per day/ 4. Transformer load 4. Cost effectiveness 5. Environmental Impact-Eco month/year management 5. Customer behaviour- Impact 4. Load shifting -Load balancing Impact analysis 6. Decarbonisation 5. Cost estimation 5. Energy consumption 6. Energy consumption 6. Cost Effectiveness patterns per patterns 7. Customer Behaviour- day/month/year 7. Demand and supply Impact analysis 6. Environmental impact monitoring 8. Anomaly Detection - Eco impact 8. Environmental Impact- 7. Decarbonisation Eco Impact What are the key interests of potential stakeholders in developing highest priority followed by comfort level (thermal, visual and smart homes? What are the techniques to classify their indoor air quality) together with other attributes such as requirements under their relevant domain concepts? environmental protection and energy cost saving. Moreover, How can we capture, manipulate and exploit most appropriate data tracking energy consumption patterns on regular basis helps to save using formal data models such as ontologies? Apart from data, how energy, especially, in residential buildings [9]. Porse et al., [10] can these ontological models help to classify the tools and carried out energy analysis of aggregated account-level utility technologies under their relevant concepts in ICT domains billing data for energy consumption across over two million What kind of graphical user interface is required to facilitate the properties in Los Angeles. They claimed that tracking energy interaction and filtration of smart homes cross-domain data? consumption patterns using account level energy usage data can help local governments devise conservation strategies. 2 STAKEHOLDER’S REQUIREMENT Research [11, 12] shows the importance of estimating cost of ANALYSIS consumed energy as well as saved energy together with cost effective energy saving strategies to achieve higher energy A continuous depletion in natural energy resources, their growing efficiency. Studies [13] also reveal that consumers even with an demand and their detrimental environmental impact due to gas adequate knowledge of how to save energy and also with a emissions and global warming, manifest a dire need of reforming professed desire to do that could still be unsuccessful to achieve energy efficiency policies especially in building sectors. This also this goal. Frederiks et al., [13] believe that this could be linked with urges potential stakeholders such as occupants, energy providers, people’s values, attitudes and intensions and their observable housing agencies, policy makers and environmental groups to set behaviours which is commonly agreed as knowledge-action gap their goals that are in consistent with the government policies and value-action gap. This is why analysing human behaviour As shown in Table 1, It is not surprising that most important factors impact is one of the key measures that concern some stakeholders that share the same importance across all stakeholders are such as occupiers and housing agencies to achieve energy householders’ health/well-being and their comfort [3], [5], [6], [7]. efficiency. Stakeholders also consider load shifting strategies [14] This implies that humans are the central and integral part of whole and anomaly detection in energy consumption patterns significant phenomena and their comfort should be given highest priority factors to improve energy efficiency. while designing a new building or starting a retrofit process. There is a growing concern about global warming and climate Amasyali and El-Gohary in 2016 [8] conducted an online survey of change which is considered an alarming threat to the ecosystem and 618 residential and office buildings in three different US states to to the people [15]. According to statistics, cities are responsible for capture occupants’ perspective on seven energy related 75-80% of climate change [16]. values/factors. They analysed that health/well-being was given Exploring Cross-Domain Data Dependencies for Smart Homes to UCIOT2017, Dec 2017, Austin, TX, USA Improve Energy Efficiency Smart Home Ontologies Main Categories n a o e ra n (I iti l M d l D fti g) Neighbourhood/Reginoal Site Information , , , City Neighbourhood