Feasibility of a micro grid scale up at campus level - Case study

Let, Dorin Dacian; Tene, B.; Husu, Adela-Gabriela; Stan, Mihai-Florin; Stancu, Laurentiu; Let, Andreea

doi:10.1109/ecai46879.2019.9041969

Cited by 3 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This disconnect presents a gap that needs to be urgently addressed to ensure compliance and to protect individual rights. Moreover, the pace at which machine learning technologies are advancing far outstrips the speed at which ethical guidelines and regulations can be developed and implemented [20]. This raises the need for ongoing, dynamic ethical considerations that can evolve in tandem with the technologies themselves.…”

Section: IImentioning

confidence: 99%

Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications

Javed,

Muqeet,

Javed

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: IImentioning

confidence: 99%

Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications

Javed,

Muqeet,

Javed

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

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

Muqeet¹,

Munir

Javed

et al. 2021

Energies

View full text Add to dashboard Cite

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.

show abstract

Study of an nZEB Office Building with Storage in Electric Vehicle Batteries and Dispatch of a Natural Gas-Fuelled Generator

Stamatellos

Stamatelos

2023

Energies

View full text Add to dashboard Cite

The rapid electrification of vehicles has led to a great increase in numbers of charging stations and a growing appetite for charging power, with stochastic charging behaviours heavily loading the electricity grid. The upcoming difficulties and increasing costs associated with electricity production will require a rapid development of smart grids and city networks. Smart micro-grids established in nearly zero-energy buildings (nZEB) are a promising strategy to support grid stability and resilience at a reduced cost. A significant amount of electricity storage capacity is necessary for optimal dispatch of the self-produced photovoltaic electricity. For office buildings, this capacity can be provided by the aggregate battery storage of the employees’ electric vehicles, which connect to the smart grid during working hours for charging and, if allowed, for discharging. An additional, fully controllable electricity source that is necessary to support an optimal micro-grid is the internal combustion engine-powered generator that is present in every commercial and office building as an emergency power supply. In the current study, a preliminary investigation of a smart micro-grid in a near zero-energy office building with a 218 kWp rooftop photovoltaic installation is carried out. The required electricity storage capacity is supplied by the employees’ electric vehicles, which stay connected to the building’s in-house chargers during working hours. The optimal rating of the natural gas-fuelled GenSet is determined based on a system’s operation and control study. Optimal dispatch of the different power sources to support the building’s autonomy and seasonal timing of electricity export to the grid is studied versus the electricity demand profiles of the electricity grid.

show abstract

Feasibility of a micro grid scale up at campus level - Case study

Cited by 3 publications

References 3 publications

Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications

Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications

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

Study of an nZEB Office Building with Storage in Electric Vehicle Batteries and Dispatch of a Natural Gas-Fuelled Generator

Contact Info

Product

Resources

About