Convolutional Neural Networks (CNNs) for power system big data analysis

The progress of the social economy and the rapid development of the power field have created more favorable conditions for the construction of my country’s power grid. In this network age, how to further realize the connection between the power system and the Internet of Things is the key content of many scholars’ research. In the Internet of Things environment, there have been many excellent results in the collection, storage, and management of electric power big data, but the problem of information security has not been completely solved. Based on big data analysis and Internet of Things technology, this paper studies the architecture design of power information security terminals. In view of the diverse types of power grid mobile information and the large amount of data, this paper designs a power transportation mobile information security management system structure, which improves the effective management of power data by the system through big data, smart sensors, and wireless communication technology. According to the experiment, the power information security terminal constructed in this paper can effectively reduce communication resources and save communication costs in the process of aggregating multidimensional data. In the user satisfaction survey, residents’ satisfaction with the convenience and safety of the intelligent power system is also as high as 9.312 and 9.233. On the whole, the application of big data and Internet of Things technology to the construction of power information security terminals can indeed improve the service efficiency of power companies under the premise of ensuring safety and allow users to have a better experience.

Download Full-text

SURVEY PAPER ON STRATEGIES FOR BIG DATA ANALYSIS AND CLUSTERINGCOMPARISON OF ARTIFICIAL NEURAL NETWORKS FOR CARDIAC ARRHYTHMIA CLASSIFICATION

International Journal of Advance Engineering and Research Development ◽

10.21090/ijaerd.23063 ◽

2017 ◽

Vol 4 (10) ◽

Cited By ~ 1

Keyword(s):

Neural Networks ◽

Big Data ◽

Artificial Neural Networks ◽

Data Analysis ◽

Cardiac Arrhythmia ◽

Big Data Analysis ◽

Survey Paper ◽

Artificial Neural

Download Full-text

A Comparison of Machine Learning Algorithms of Big Data for Time Series Forecasting Using Python

Open Source Software for Statistical Analysis of Big Data - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-2768-9.ch007 ◽

2020 ◽

pp. 197-218

Author(s):

Son Nguyen ◽

Anthony Park

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Time Series ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Time Series Forecasting ◽

Machine Learning Algorithms ◽

Time Series Models ◽

Data Sets

This chapter compares the performances of multiple Big Data techniques applied for time series forecasting and traditional time series models on three Big Data sets. The traditional time series models, Autoregressive Integrated Moving Average (ARIMA), and exponential smoothing models are used as the baseline models against Big Data analysis methods in the machine learning. These Big Data techniques include regression trees, Support Vector Machines (SVM), Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), and long short-term memory neural networks (LSTM). Across three time series data sets used (unemployment rate, bike rentals, and transportation), this study finds that LSTM neural networks performed the best. In conclusion, this study points out that Big Data machine learning algorithms applied in time series can outperform traditional time series models. The computations in this work are done by Python, one of the most popular open-sourced platforms for data science and Big Data analysis.

Download Full-text