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

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Computer Systems Science & Engineering 2023年第11期47卷 1751-1773页

作者： Muhammad Munsif Fath U Min ullah samee ullah khan Noman khan Sung Wook Baik Department of Software Convergence Sejong UniversitySeoul143-747Korea Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul143-747Korea

Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and ***,weather fluctuations make large-scale PV power integration and management challenging despite the economic *** PV forecasting techniques(sequential and convolutional neural networks(CNN))are sensitive to environmental conditions,reducing energy distribution system *** handle these issues,this article proposes an efficient,weather-resilient convolutional-transformer-based network(CT-NET)for accurate and efficient PV power *** network consists of three main ***,the acquired PV generation data are forwarded to the pre-processing module for data ***,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 *** 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 ***,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 *** 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.

关键词： Solar energy forecasting renewable energy systems photovoltaic generation forecasting time series data transformer models deep learning machine learning

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A V2I communication-based pipeline model for adaptive urban traffic light scheduling

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Frontiers of Computer Science 2019年第5期13卷 929-942页

作者： Libing WU Lei NIE samee U. khan Osman KHALID Dan WU School of Cyber Science and Engineering Wuhan University Wuhan 430072 China School of Computer Science Wuhan University Wuhan 430072 China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Department of Electrical and Computer Engineering North Dakota State University Fargo USA COMS ATS Institute of Information Technology Islamabad Pakistan School of Computer Science University of Windsor Windsor Ontario Canada

Adaptive traffic light scheduling based on realtime traffic information processing has proven effective for urban traffic congestion management. However, fine-grained information regarding individual vehicles is difficult to acquire through traditional data collection techniques and its accuracy cannot be guaranteed because of congestion and harsh environments. In this study, we first build a pipeline model based on vehicle-to-infrastructure communication, which is a salient technique in vehicular adhoc networks. This model enables the acquisition of fine-grained and accurate traffic information in real time via message exchange between vehicles and roadside units. We then propose an intelligent traffic light scheduling method (ITLM) based on a “demand assignment” principle by considering the types and turning intentions of vehicles. In the context of this principle, a signal phase with more vehicles will be assigned a longer green time. Furthermore, a green-way traffic light scheduling method (GTLM) is investigated for special vehicles (e.g., ambulances and fire engines) in emergency scenarios. Signal states will be adjusted or maintained by the traffic light control system to keep special vehicles moving along smoothly. Comparative experiments demonstrate that the ITLM reduces average wait time by 34%-78% and average stop frequency by 12%-34% in the context of traffic management. The GTLM reduces travel time by 22%^44% and 30%-55% under two types of traffic conditions and achieves optimal performance in congested scenarios.

关键词： traffic light scheduling vehicular ad hoc networks pipeline model vehicle-to-infrastructure communication intersection

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