CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information

Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and organizations.However,weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits.Existing PV forecasting techniques(sequential and convolutional neural networks(CNN))are sensitive to environmental conditions,reducing energy distribution system performance.To handle these issues,this article proposes an efficient,weather-resilient convolutional-transformer-based network(CT-NET)for accurate and efficient PV power forecasting.The network consists of three main modules.First,the acquired PV generation data are forwarded to the pre-processing module for data refinement.Next,to carry out data encoding,a CNNbased multi-head attention(MHA)module is developed in which a single MHA is used to decode the encoded data.The encoder module is mainly composed of 1D convolutional and MHA layers,which extract local as well as contextual features,while the decoder part includes MHA and feedforward layers to generate the final prediction.Finally,the performance of the proposed network is evaluated using standard error metrics,including the mean squared error(MSE),root mean squared error(RMSE),and mean absolute percentage error(MAPE).An ablation study and comparative analysis with several competitive state-of-the-art approaches revealed a lower error rate in terms of MSE(0.0471),RMSE(0.2167),and MAPE(0.6135)over publicly available benchmark data.In addition,it is demonstrated that our proposed model is less complex,with the lowest number of parameters(0.0135 M),size(0.106 MB),and inference time(2 ms/step),suggesting that it is easy to integrate into the smart grid.

Interobserver reliability between pediatric radiologists and residents in ultrasound evaluation of intraventricular hemorrhage in premature infants

BACKGROUND Germinal matrix intraventricular hemorrhage(IVH)may contribute to significant morbidity and mortality in premature infants.Timely identification and grading of IVH affect decision-making and clinical outcomes.There is possibility of misinterpretation of the ultrasound appearances,and the interobserver variability has not been investigated between radiology resident and board-certified radiologist.AIM To assess interobserver reliability between senior radiology residents performing bedside cranial ultrasound during on-call hours and pediatric radiologists.METHODS From June 2018 to June 2020,neonatal cranial ultrasound examinations were performed in neonatal intensive care unit.Ultrasound findings were recorded by the residents performing the ultrasound and the pediatric attending radiologists.RESULTS In total,200 neonates were included in the study,with a mean gestational age of 30.9 wk.Interobserver agreement for higher grade(Grade III&IV)IVH was excellent.There was substantial agreement for lower grade(Grade I&II)IVH.CONCLUSION There is strong agreement between radiology residents and pediatric radiologists,which is higher for high grade IVHs.