Knoholem: Knowledge-Based Energy Management for Public Buildings Through Holistic Information Modeling and 3D Visualization

Anzaldi, Gabriel; Corchero, Aitor; Wicaksono, Hendro; McGlinn, Kris; Gerdelan, Anton; Dibley, Michael James

doi:10.1007/978-3-319-02332-8_5

Cited by 4 publications

(2 citation statements)

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“…The research and development of a novel BEMS was undertaken through an EC FP7 project [50] and tested within a mixed mode residential care home in The Netherlands. The project aimed to produce a BEMS, which could be retrofitted into public buildings with minimal investment, to exploit an enhanced sensing infrastructure and the existing BEMS, augmented with analytics and visualization components through a semantic Web approach.…”

Section: Overview Of Proposed Solutionmentioning

confidence: 99%

User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization

Howell

Wicaksono

Yüce

et al. 2019

IEEE Trans. Cybern.

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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.

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Section: Overview Of Proposed Solutionmentioning

confidence: 99%

User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization

Howell

Wicaksono

Yüce

et al. 2019

IEEE Trans. Cybern.

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“…It takes distributed renewable energy as the primary energy source and is tightly coupled with natural gas networks, transportation networks, and other systems. This complex structure poses severe challenges for energy management [30][31][32][33], including complex context modeling, heterogeneous data analysis, distributed nodes integration, and dynamic controlling. The great achievement of semantic technologies obtained in the area of energy management verified the advantages of knowledge engineering in complex system scheduling and controlling.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management

et al. 2021

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Today, the automotive and transportation sector is undergoing a transformation process to meet the requirements of sustainable and efficient operations. This transformation mainly reveals itself by electric vehicles, hybrid electric vehicles, and electric vehicle sharing. One significant, and the most expensive, component in electric vehicles is the batteries, and the management of batteries is crucial. It is essential to perform constant monitoring of behavior changes for operational purposes and quickly adjust components and operations to these changes. Thus, to address these challenges, we propose a knowledge graph-based data integration framework for simplifying access and analysis of data accumulated through the operations of vehicles and related transportation systems. The proposed framework aims to enable the effortless analysis and navigation of integrated knowledge and the creation of additional data sets from this knowledge to use during the application of data analysis and machine learning. The knowledge graph serves as a significant component to simplify the extraction, enrichment, exploration, and generation of data in this framework. We have developed it according to the human-centered design, and various roles of the data science and machine learning life cycle can use it. Its main objective is to streamline the exploration and interaction with the integrated data to maximize human productivity. Finally, we present a battery use case to show the feasibility and benefits of the proposed framework. The use case illustrates the usage of the framework to extract knowledge from raw data, navigate and enrich it with additional knowledge, and generate data sets.

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