Generation of sub-item load profiles for public buildings based on the conditional generative adversarial network and moving average method

Zhang, Yuhang; Deng, Xiangtian; Zhang, Yi

doi:10.1016/j.enbuild.2022.112185

Cited by 10 publications

(1 citation statement)

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“…Thus, the daily pollen deposition was extended to a 2–4 days moving average deposition to increase data and reduce the influence of singular values (e.g., the 2‐day average pollen deposition represented the average pollen grains of the current and previous days). 19 , 20 Additionally, patient data obtained on holidays were excluded because of differences in the hospital capacity on holidays and weekdays. Ultimately, a total of 111 paired samples met the eligibility requirements.…”

Section: Methodsmentioning

confidence: 99%

Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3‐day moving predictive model

Yin

Ouyang

Dang

et al. 2023

Clinical & Translational All

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Background: Artemisia pollen is the most prevalent outdoor aeroallergen causing respiratory allergies in Beijing, China. Pollen allergen concentrations have a direct impact on the quality of life of those suffering from allergies. Artemisia pollen deposition grading predictions can provide early warning for the protection and treatment of patients as well as provide a scientific basis for allergen specific clinical immunotherapy.Objective: To develop a model of Artemisia pollen grading to predict development in patients with pollen allergy.Methods: Artemisia pollen data from four pollen monitoring stations in Beijing as well as the number of Artemisia pollen allergen serum specific immunoglobulin E positive cases in Beijing Tongren Hospital from 2014 to 2016 were used to develop a statistical model of pollen deposition and provide optimised threshold values. Results: A logarithmic correlation existed between the number of patients withArtemisia pollen allergy and Artemisia pollen deposition, and the average pollen deposition for three consecutive days was most correlated with the number of allergic patients. Based on the threshold of the number of patients and the characteristics of Artemisia pollen, a five-stage pollen deposition grading model was developed to predict the degree of pollen allergy.Conclusions: Graded prediction of pollen deposition may help pollen allergic populations benefit from preventive interventions before onset.

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Section: Methodsmentioning

confidence: 99%