Showing items related by title, author, creator and subject.

• AI-UBREM Model For Positive Energy District, Case Study: City of Vienna

  Sharma, Naitik; lopez, Alexander; Khean, Naridh; Jastrzebska, Aleksandra; Duering, Serjoscha; Chronis, Angelos; Leon, David; Allam, Sammar; College Collaboration; Architecture (25/6/2022)

  Housing in Austria contributes to 34% of Vienna's Energy consumption. Urban Building Energy modeling (UBEM) demonstrated a reliable tool to visualize city fabric and its complex systems use patterns. This tool enables visualizing and prediction of energy and solar potential of buildings as an AI-based UBREM. U-value is an index that measures building envelop/ structure heat transmittance. The lower the u-value shows better insulation, thus decreasing buildings' energy consumption. Use the sliders to alter u-values for walls, windows, roof, and basement. A single good insulation layer added to your wall can decrease building energy consumption by almost 30%. Hence, building energy demand (BED) can be met using PV cells on rooftops, the PV potential can even exceed BED and support neighboring units manifesting a Positive Energy District (PED) with renewable energy (RE).
• Smart energy solutions: two-way energy information exchange between utility companies, consumers, and prosumers

  Kashef, M, O, A Troisi, Visvisi; External Collaboration; NA; 0; 0; Architecture; 0; Kashef, Mohamad (Routledge, 2023-06-01)

  Smart cities are gradually but surely developing the infrastructure and system architecture required for integrating public and private energy services. With the mounting evidence that fossil fuels are detrimental to the environment, it is imperative to integrate renewable energy sources with existing utility infrastructure. The monopoly of utility companies on energy production and distribution is being eroded due to the proliferation of renewable energy sources (RES) from private prosumers (producers/consumers). Prosumers have developed some capacity to generate a power surplus that exceeds their immediate needs. Individuals and group prosumers have created energy communities with infrastructural and technological ecosystems that allow them to generate, control, monitor, and trade power over private and public utility networks. Multi-layered wireless mesh networks (WMN) that connect multi-sensor modules (MSM) and big data analytics servers with built in AI capacity are facilitating the development of smart energy solutions. They will revolutionize the energy sector and reconfigure the process of energy production, distribution, and information sharing among individuals, communities, and existing utility companies. Considering the fact that (i) the pace of urbanization increases, (ii) energy demand in (smart) urban spaces grows, and (iii) prosumers and, so energy communities, play an ever more important role also in the (smart) city context space, the objective of this chapter is to review the existing smart energy systems and the prospect of their application in the smart city space. The notions of energy supply and demand for energy and the role of energy communities will form the thread of the discussion in this chapter.
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  Investigating The Best Statistical Lifetime Model for Commercial Lithium-Ion Batteries

  Kittaneh, Omar; Abdulmajid, Mohammed; Mouais, Talal Ali; Graduate Studies and Research (Effat University, 2021)

  One of the significant drawbacks of renewable energy is that renewable energy sources such as solar and wind are intermittent and operate with different degrees of intermittency. In other words, they only generate power when the sun is shining or when the wind is blowing. One of the most promising methods of overcoming the problem intermittent renewable energy supplies is the use batteries that can store renewable energy until it is needed. Batteries are known for their high commercial potential, fast response time, modularity, flexible installation, and short construction cycles. Consequently, the battery is an attractive option for storing renewable energy and peak shaving during intensive grid loads, and it can serve as a back-up system to control voltage drops in the energy grid. The lithium-ion battery is regarded as one of the most promising battery technologies because it has a high specific energy density, a high volumetric energy density, a low and falling cost, and a long lifetime. The failure mechanism of the Li-ion battery is a highly complex phenomenon produced by a complex interplay of many physical and chemical mechanisms. Three main approaches are used to modeling the lifetime of batteries: the physics-based model (Electrochemical modeling); the half empirical model (statistical methods), which is based on conducting and analyzing battery aging experiments; and the data-driven model, which is based on numerous battery aging experiments that require data analysis and machine learning. This study employs the statistical method to understand and uncover hidden failure interactions inside the cell. It is used here because of its relative simplicity. Based on Accelerated lifetime data of Li-ion adapted from [10], we examine the fitting of three lifetime distributions; the Weibull, lognormal and normal distributions. We conclude that the lognormal distribution is the best lifetime model for Li-ion batteries. Also, this study shows that the electrode physical parameters, such as thickness, play an important role in the lifetime model of Li-ion battery.