Integrating building and urban semantics to empower smart water solutions

Current urban water research involves intelligent sensing, systems integration, proactive users and data-driven management through advanced analytics. The convergence of building information modeling with the smart water field provides an opportunity to transcend existing operational barriers. Such research would pave the way for demand-side management, active consumers, and demand-optimized networks, through interoperability and a system of systems approach. This paper presents a semantic knowledge management service and domain ontology which support a novel cloud-edge solution, by unifying domestic socio-technical water systems with clean and waste networks at an urban scale, to deliver value-added services for consumers and network operators. The web service integrates state of the art sensing, data analytics and middleware components. We propose an ontology for the domain which describes smart homes, smart metering, telemetry, and geographic information systems, alongside social concepts. This integrates previously isolated systems as well as supply and demand-side interventions, to improve system performance. A use case of demand-optimized management is introduced, and smart home application interoperability is demonstrated, before the performance of the semantic web service is presented and compared to alternatives. Our findings suggest that semantic web technologies and IoT can merge to bring together large data models with dynamic data streams, to support powerful applications in the operational phase of built environment systems

Usability evaluation of a web-based tool for supporting holistic building energy management

This paper presents the evaluation of the level of usability of an intelligent monitoring and control interface for energy efficient management of public buildings, called BuildVis, which forms part of a Building Energy Management System (BEMS.) The BEMS ‘intelligence’ is derived from an intelligent algorithm component which brings together ANN-GA rule generation, a fuzzy rule selection engine, and a semantic knowledge base. The knowledge base makes use of linked data and an integrated ontology to uplift heterogeneous data sources relevant to building energy consumption. The developed ontology is based upon the Industry Foundation Classes (IFC), which is a Building Information Modelling (BIM) standard and consists of two different types of rule model to control and manage the buildings adaptively. The populated rules are a mix of an intelligent rule generation approach using Artificial Neural Network (ANN) and Genetic Algorithms (GA), and also data mining rules using Decision Tree techniques on historical data. The resulting rules are triggered by the intelligent controller, which processes available sensor measurements in the building. This generates ‘suggestions’ which are presented to the Facility Manager (FM) on the BuildVis web-based interface. BuildVis uses HTML5 innovations to visualise a 3D interactive model of the building that is accessible over a wide range of desktop and mobile platforms. The suggestions are presented on a zone by zone basis, alerting them to potential energy saving actions. As the usability of the system is seen as a key determinate to success, the paper evaluates the level of usability for both a set of technical users and also the FMs for five European buildings, providing analysis and lessons learned from the approach taken

Towards the next generation of smart grids: semantic and holonic multi-agent management of distributed energy resources

The energy landscape is experiencing accelerating change; centralized energy systems are being decarbonized, and transitioning towards distributed energy systems, facilitated by advances in power system management and information and communication technologies. This paper elaborates on these generations of energy systems by critically reviewing relevant authoritative literature. This includes a discussion of modern concepts such as ‘smart grid’, ‘microgrid’, ‘virtual power plant’ and ‘multi-energy system’, and the relationships between them, as well as the trends towards distributed intelligence and interoperability. Each of these emerging urban energy concepts holds merit when applied within a centralized grid paradigm, but very little research applies these approaches within the emerging energy landscape typified by a high penetration of distributed energy resources, prosumers (consumers and producers), interoperability, and big data. Given the ongoing boom in these fields, this will lead to new challenges and opportunities as the status-quo of energy systems changes dramatically. We argue that a new generation of holonic energy systems is required to orchestrate the interplay between these dense, diverse and distributed energy components. The paper therefore contributes a description of holonic energy systems and the implicit research required towards sustainability and resilience in the imminent energy landscape. This promotes the systemic features of autonomy, belonging, connectivity, diversity and emergence, and balances global and local system objectives, through adaptive control topologies and demand responsive energy management. Future research avenues are identified to support this transition regarding interoperability, secure distributed control and a system of systems approach

User centered neuro-fuzzy energy management through semantic-based optimization

This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system’s intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings

Water utility decision support through the semantic web of things

Urban environments are urgently required to become smarter. However, building advanced applications on the Internet of Things requires seamless interoperability. This paper proposes a water knowledge management platform which extends the Internet of Things towards a Semantic Web of Things, by leveraging the semantic web to address the heterogeneity of web resources. Proof of concept is demonstrated through a decision support tool which leverages both the data-driven and knowledge-based programming interfaces of the platform. The solution is grounded in a comprehensive ontology and rule base developed with industry experts. This is instantiated from GIS, sensor, and EPANET data for a Welsh pilot. The web service provides discoverability, context, and meaning for the sensor readings stored in a scalable database. An interface displays sensor data and fault inference notifications, leveraging the complementary nature of serving coherent lower and higher-order knowledge

Robust requirements gathering for ontologies in smart water systems

Urban environments are urgently required to become smarter in order to overcome sustainability and resilience challenges whilst remaining economically viable. This involves a vast increase in the penetration of ICT resources, both physical and virtual, with the requirement to factor in built environment, socio-economic and human artefacts. This paper therefore proposes a methodology for eliciting, testing, and deploying, requirements in the field of urban cybernetics. This extends best practice requirements engineering principles in order to meet the demands of this growing niche. The paper follows a case study approach of applying the methodology in the smart water domain, where it achieves positive results. The approach heavily utilises iteration alongside domain experts, but also mandates the integration of technical domain experts to ensure software requirements are met. A key novelty of the approach is prioritising a balance between: a) knowledge engineers’ tenacity for logical accuracy, b) software engineers’ need for speed, simplicity, and integration with other components, and c) the domain experts’ needs in order to invoke ownership and hence nurture adoption of the resulting ontology

The politics of Chinese trade and the Asian financial crises : questioning the wisdom of export-led growth

Between 1987 and 1996 Chinese exports increased by an average of 14% each year. During this decade, export growth became a crucial determinant of overall economic growth. However, as a consequence of the East Asian financial crises, Chinese export growth slowed, threatening the successful implementation of plans to restructure the domestic Chinese economy. This paper traces the reasons for the rapid growth and subsequent slowing of Chinese exports, and asks whether the strategy provides a solid basis for the long term development of the Chinese economy. In particular, the paper focuses on the role and significance of the processing trade in boosting Chinese exports. The high proportion of imported components in processed exports questions whether China is really benefiting as much from export growth as aggregate trade figures seem to suggest

Recruitment to the “Breast—Activity and Healthy Eating After Diagnosis” (B-AHEAD) Randomized Controlled Trial

Excess weight at breast cancer diagnosis and weight gain during treatment are linked to increased breast cancer specific and all-cause mortality. The Breast—Activity and Healthy Eating After Diagnosis (B-AHEAD) trial tested 2 weight loss diet and exercise programmes versus a control receiving standard written advice during adjuvant treatment. This article identifies differences in characteristics between patients recruited from the main trial site to those of the whole population from that site during the recruitment period and identifies barriers to recruitment. A total of 409 patients with operable breast cancer were recruited within 12 weeks of surgery. We compared demographic and treatment factors between women recruited from the main trial coordinating site (n = 300) to the whole breast cancer population in the center (n = 532). Uptake at the coordinating site was 42%, comparable to treatment trials in the unit (47%). Women recruited were younger (55.9 vs 61.2 years, P < .001), more likely to live in least deprived postcode areas (41.7% vs 31.6%, P = .004), and more likely to have screen-detected cancers (55.3% vs 48.7%, P = .026) than the whole breast cancer population. The good uptake highlights the interest in lifestyle change around the time of diagnosis, a challenging time in the patient pathway, and shows that recruitment at this time is feasible. Barriers to uptake among older women and women with a lower socioeconomic status should be understood and overcome in order to improve recruitment to future lifestyle intervention programs