Amylin is a pancreatic β-cell hormone that produces effects in several different organ systems. Here, we review the literature in rodents and in humans on amylin research since its discovery as a ...hormone about 25 years ago. Amylin is a 37-amino-acid peptide that activates its specific receptors, which are multisubunit G protein-coupled receptors resulting from the coexpression of a core receptor protein with receptor activity-modifying proteins, resulting in multiple receptor subtypes. Amylin's major role is as a glucoregulatory hormone, and it is an important regulator of energy metabolism in health and disease. Other amylin actions have also been reported, such as on the cardiovascular system or on bone. Amylin acts principally in the circumventricular organs of the central nervous system and functionally interacts with other metabolically active hormones such as cholecystokinin, leptin, and estradiol. The amylin-based peptide, pramlintide, is used clinically to treat type 1 and type 2 diabetes. Clinical studies in obesity have shown that amylin agonists could also be useful for weight loss, especially in combination with other agents.
Display omitted
•A-UBEM generates synthetic smart meter data for all buildings in a city.•We integrate data-driven and physics-based simulation methods to create A-UBEM.•A-UBEM is built using solely ...open-data sources, with New York City as a case study.•We validate our model using Monte Carlo simulations and city-wide hourly load.•We highlight applications where A-UBEM can be used to plan sustainable cities.
Cities officials are increasingly interested in understanding spatial and temporal energy patterns of the built environment to facilitate their city’s transition to a low-carbon future. In this paper, a new Augmented-Urban Building Energy Model (A-UBEM) is proposed that combines data-driven and physics-based simulation methods to produce synthetic hourly load curve estimates for every building within a city—similar to data an hourly smart meter would measure. By using only publicly available data, a generalizable two-step process is implemented—that other cities with similar available data can replicate—using New York City as a case study. Step (1) estimates the annual energy use for every building in the city using supervised machine learning algorithms. Step (2) extends these results and leverages physics-based simulation models through a convex optimization formulation that minimizes the squared difference between the aggregated building demand and the observed city-wide hourly electricity demand. Results from step (1) show that the Random Forest algorithm performs best with a mean log squared error of 0.293, while the convex optimization in step (2) results in a mean training error of 6.11% mean absolute percentage error (MAPE). To validate the stability of the produced load curves, Monte Carlo simulations are conducted, using random subsets of buildings from the city, which produce an out-of-sample error averaging 6.41% MAPE across each simulation. Particle swarm optimization is also explored—using the results from the Monte Carlo simulation—to assess if the model could be improved by relaxing certain constraints, but marginal error reductions are found, further proving the stability of the proposed model. Overall, A-UBEM is a first step towards creating highly granular urban-scale synthetic hourly load curves solely using open data. Such load curves are integral for planning sustainable cities and accelerating the adoption of low-carbon distributed energy resources (DERs) and district energy systems.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PDF
