Advanced Analytics at the Edge
IWSSIP（2023）
Comsensus d.o.o.
Abstract
This paper discusses the importance of Big Data processing and analysis at the edge of the electric power grid due to the increasing integration of renewable energy sources, electric vehicles, and new loads that enable a greener transition. The volume, velocity, and variety of data generated by the electric power system (EPS) is rapidly increasing and real-time processing and analysis is necessary to improve system efficiency, reliability, and security. This paper presents an advanced edge cloud computing framework that addresses quality of service challenges and tackles some of the Big Data challenges. The framework enables various instantiation scenarios and consists of open-source tools for managing and automating the EdgeCloud infrastructure. A case study of a 50 kWp photovoltaic power plant is used to demonstrate the effectiveness of the framework in processing and analyzing data at the edge. Three different analytic tools are presented that address real-time and batch processing at the edge to offload data processing and data availability from the cloud to the edge. The paper concludes that edge computing plays a critical role in modernizing EPS and paving the way for a more sustainable and resilient energy future.
MoreTranslated text
Key words
Big Data,Cloud,Edge,Forecasting,Predictive maintenance,PMU
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined