Forecasting energy consumption demand of customers in smart grid using Temporal Fusion Transformer (TFT)

Energy consumption prediction has always remained a concern for researchers because of the rapid growth of the human population and customers joining smart grids network for smart home facilities. Recently, the spread of COVID-19 has dramatically increased energy consumption in the residential sector. Hence, it is essential to produce energy per the residential customers' requirements, improve economic efficiency, and reduce production costs. The previously published papers in the literature have considered the overall energy consumption prediction, making it difficult for production companies to produce energy per customers' future demand. Using the proposed study, production companies can accurately have energy per their customers' needs by forecasting future energy consumption demands. Scientists and researchers are trying to minimize energy consumption by applying different optimization and prediction techniques;hence this study proposed a daily, weekly, and monthly energy consumption prediction model using Temporal Fusion Transformer (TFT). This study relies on a TFT model for energy forecasting, which considers both primary and valuable data sources and batch training techniques. The model's performance has been related to the Long Short-Term Memory (LSTM), LSTM interpretable, and Temporal Convolutional Network (TCN) models. The model's performance has remained better than the other algorithms, with mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) of 4.09, 2.02, and 1.50. Further, the overall symmetric mean absolute percentage error (sMAPE) of LSTM, LSTM interpretable, TCN, and proposed TFT remained at 29.78%, 31.10%, 36.42%, and 26.46%, respectively. The sMAPE of the TFT has proved that the model has performed better than the other deep learning models. © 2023 The Author(s)

Type 2 diabetes and COVID-19 related mortality in solapur: A whole population study

Presently large-scale study concludes that there are many factors associated with an increased risk of COVID-19-related in-hospital death. Patients with comorbidities may be more susceptible to the COVID- 19 complications and may show compromised immune competency. The aim of the study is to find out increased relative and absolute risks of COVID-19, associated with Diabetes Mellitus in Solapur city. Study design is Cross sectional study. Study group included all patients with COVID -19 diabetic and non-diabetic patients in Solapur. It was observed that among all diabetic patients with COVID-19 fever was the most common initial symptoms;fever (100%) followed by cough (97.16%), Breathlessness (89.05%), dyspnea (9.81%),muscular soreness (6.22%) and chest distress(7.92%). In Diabetic with COVID-19, age distribution of the patients was in between 55-80 years, in non-diabetic COVID patients age group ranges 0-76 years and mortality rate observed in age group with 54-80 years. The present data showed higher mortality rate in patients of Diabetes Mellitus with COVID-19. Further it was observed that the mortality rate was higher in male (11.77 %) as compare to female (10.07 %.). Our study concludes that COVID-19 patients with Diabetes Mellitus are more prone for morbidity as compared with non-diabetic COVID patients. Diabetes Mellitus in COVID- 19 patients increases the susceptibility of complications with concomitant decrease immune system. The total number of COVID- 19 death recorded in Solapur included majority of the patients with Diabetes Mellitus as one of the responsible factor in worsening of the disease.

An interpretable mortality prediction model for COVID-19 patients in Solapur-Maharashtra

The aim of the study is to find out the causes of mortality in COVID-19 patients of Solapur, to compare the mortality rate of Solapur with patients all over India and the factors affecting the increase mortality rate. Study group included patients with COVID-19 deaths in Solapur, death of COVID-19 with complications like Myocardial infarction, Hypertension, coronary artery disease, Diabetes Mellitus & Asthma. The morbidity rate was highest in COVID-19 patients with diabetes mellitus (53 %) and hypertension (36%). The morbidity rate in COVID-19 was also significant in patients with cardiovascular (5.8%), Asthma (1.4%) and cerebrovascular disease. The patients with renal and other complications were also affected and the mortality rate was (1.8 %) in these patients. Our findings showed that most of the patients who died with COVID-19 were having many other complications. Diabetes mellitus itself contributed fifty three percent, whereas hypertensive patients thirty three percent. Apart from this there were patients with other complications also. This shows that the mortality rate in Solapur was high because of old age and comorbid patients.