Microgrid design considerations for a smart-energy university campus

Gonzalez, R. Morales; Goch, T.A.J. van; Aslam, Muhammad; Blanch, A.; Ribeiro, Paulo F.

doi:10.1109/isgteurope.2014.7028743

Cited by 18 publications

(6 citation statements)

References 12 publications

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“…These applications are used for controlling and monitoring the generation, distribution, and consumption of the campus energy sources (e.g., photovoltaic systems or wind generators). Specifically, in the last years, multiple authors focused their research on the study of smart campus microgrids [80][81][82], smart grids [83,84], and smart energy systems [85,86]. •…”

Section: Smart Campus and University Applicationsmentioning

confidence: 99%

Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

2019

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Smart campuses and smart universities make use of IT infrastructure that is similar to the one required by smart cities, which take advantage of Internet of Things (IoT) and cloud computing solutions to monitor and actuate on the multiple systems of a university. As a consequence, smart campuses and universities need to provide connectivity to IoT nodes and gateways, and deploy architectures that allow for offering not only a good communications range through the latest wireless and wired technologies, but also reduced energy consumption to maximize IoT node battery life. In addition, such architectures have to consider the use of technologies like blockchain, which are able to deliver accountability, transparency, cyber-security and redundancy to the processes and data managed by a university. This article reviews the state of the start on the application of the latest key technologies for the development of smart campuses and universities. After defining the essential characteristics of a smart campus/university, the latest communications architectures and technologies are detailed and the most relevant smart campus deployments are analyzed. Moreover, the use of blockchain in higher education applications is studied. Therefore, this article provides useful guidelines to the university planners, IoT vendors and developers that will be responsible for creating the next generation of smart campuses and universities.

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Section: Smart Campus and University Applicationsmentioning

confidence: 99%

Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

2019

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“…Anonymization involves the removal or modification of personal information from a database, rendering it anonymous. However, both methods have their limitations [27]. Encrypted data must be decrypted for use, exposing it to potential breaches at that stage.…”

Section: Data Privacy and Securitymentioning

confidence: 99%

“…Should healthcare providers always have the final say, even if the algorithm has a statistically higher success rate in certain diagnostic or treatment scenarios? These ethical questions become even more critical when we consider the increasing reliance on remote healthcare services, where machine algorithms may be the first point of diagnostic contact [10]. The burgeoning field of research in this area has begun to generate ethical frameworks designed to navigate these complex issues.…”

Section: Introductionmentioning

confidence: 99%

Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications

Javed,

Muqeet,

Javed

et al. 2024

IEEE Access

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The application of Machine Learning (ML) in healthcare has opened unprecedented avenues for predictive analytics, diagnostics, and personalized medicine. However, the sensitivity of healthcare data and the ethical dilemmas associated with automated decision-making necessitate a rigorous ethical framework. This review paper aims to provide a comprehensive overview of the existing ethical frameworks that guide ML in healthcare and evaluates their adequacy in ad-dressing ethical challenges. Specifically, this article offers an in-depth examination of prevailing ethical constructs that oversee healthcare ML, spotlighting pivotal concerns: data protection, in-formed assent, equity, and patient autonomy. Various analytical approaches including quantitative metrics, statistical methods for bias detection, and qualitative thematic analyses are applied to address these challenges. Insights are further enriched through case studies of Clinical Decision Support Systems, Remote Patient Monitoring, and Telemedicine Applications. Each case is evaluated against existing ethical frameworks to identify limitations and gaps. Based on our com-prehensive review and evaluation, we propose actionable recommendations for evolving ethical guidelines. The paper concludes by summarizing key findings and underscoring the urgent need for robust ethical frameworks to guide ML applications in sensitive healthcare environments. Future work should focus on the development and empirical validation of new ethical frameworks that can adapt to emerging technologies and ethical dilemmas in healthcare ML.

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“…2 MT denotes the microturbine. 3 EV denotes the electric Campus [33] Eindhoven University of Technology, The Netherlands Energies 2021, 14, x FOR PEER REVIEW 7 of 35 [39] Clemson University, South Carolina…”

Section: Sc Denotes Supementioning

confidence: 99%

An Energy Management System of Campus Microgrids: State-of-the-Art and Future Challenges

Muqeet¹,

Munir

Javed

et al. 2021

Energies

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The multiple uncertainties in a microgrid, such as limited photovoltaic generations, ups and downs in the market price, and controlling different loads, are challenging points in managing campus energy with multiple microgrid systems and are a hot topic of research in the current era. Microgrids deployed at multiple campuses can be successfully operated with an exemplary energy management system (EMS) to address these challenges, offering several solutions to minimize the greenhouse gas (GHG) emissions, maintenance costs, and peak load demands of the microgrid infrastructure. This literature survey presents a comparative analysis of multiple campus microgrids’ energy management at different universities in different locations, and it also studies different approaches to managing their peak demand and achieving the maximum output power for campus microgrids. In this paper, the analysis is also focused on managing and addressing the uncertain nature of renewable energies, considering the storage technologies implemented on various campuses. A comparative analysis was also considered for the energy management of campus microgrids, which were investigated with multiple optimization techniques, simulation tools, and different types of energy storage technologies. Finally, the challenges for future research are highlighted, considering campus microgrids’ importance globally. Moreover, this paper is expected to open innovative paths in the future for new researchers working in the domain of campus microgrids.

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Microgrid design considerations for a smart-energy university campus

Cited by 18 publications

References 12 publications

Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications

An Energy Management System of Campus Microgrids: State-of-the-Art and Future Challenges

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