scholarly journals Big Data Analysis of Mid-Long Term Load Forecasting in Power System

Smart Grid ◽  
2014 ◽  
Vol 04 (06) ◽  
pp. 207-215
Author(s):  
小平 王
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Power System ◽  
Load Forecasting ◽  
Big Data Analysis

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

2015 ◽  
Vol 87 (3) ◽  
pp. 815-835 ◽  
Author(s):  
Pavel Baltiiski ◽  
Ilia Iliev ◽  
Boian Kehaiov ◽  
Vladimir Poulkov ◽  
Todor Cooklev
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Access Networks ◽  
Spectrum Monitoring ◽  
Radio Access Networks ◽  
Radio Access

scholarly journals Information Security Terminal Architecture of Power Transportation Mobile Internet of Things Based on Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xianzhi Tang ◽  
Chunyan Ding
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Information Security ◽  
Internet Of Things ◽  
Power System ◽  
Big Data Analysis ◽  
The Internet ◽  
Mobile Information ◽  
Internet Of Things Technology ◽  
The Internet Of Things

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.

scholarly journals Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN

2019 ◽  
Vol 3 (3) ◽  
pp. 36 ◽  
Author(s):  
Muhammad Waseem ◽  
Zhenzhi Lin ◽  
Li Yang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Power Systems ◽  
Demand Response ◽  
Load Forecasting ◽  
Big Data Analysis ◽  
Statistical Regression ◽  
Air Conditioners ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt

Air Conditioners (AC) impact in overall electricity consumption in buildings is very high. Therefore, controlling ACs power consumption is a significant factor for demand response. With the advancement in the area of demand side management techniques implementation and smart grid, precise AC load forecasting for electrical utilities and end-users is required. In this paper, big data analysis and its applications in power systems is introduced. After this, various load forecasting categories and various techniques applied for load forecasting in context of big data analysis in power systems have been explored. Then, Levenberg–Marquardt Algorithm (LMA)-based Artificial Neural Network (ANN) for residential AC short-term load forecasting is presented. This forecasting approach utilizes past hourly temperature observations and AC load as input variables for assessment. Different performance assessment indices have also been investigated. Error formulations have shown that LMA-based ANN presents better results in comparison to Scaled Conjugate Gradient (SCG) and statistical regression approach. Furthermore, information of AC load is obtainable for different time horizons like weekly, hourly, and monthly bases due to better prediction accuracy of LMA-based ANN, which is helpful for efficient demand response (DR) implementation.

scholarly journals LANDSAT BIG DATA ANALYSIS FOR DETECTING LONG-TERM WATER QUALITY CHANGES: A CASE STUDY IN THE HAN RIVER, SOUTH KOREA

2017 ◽  
Vol IV-1/W1 ◽  
pp. 83-89
Author(s):  
J. C. Seong ◽  
C. S. Hwang ◽  
R. Gibbs ◽  
K. Roh ◽  
M. R. Mehdi ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
South Korea ◽  
Chlorophyll A ◽  
Water Clarity ◽  
Big Data Analysis ◽  
Near Infrared Reflectance ◽  
Han River

Landsat imagery satisfies the characteristics of big data because of its massive data archive since 1972, continuous temporal updates, and various spatial resolutions from different sensors. As a case study of Landsat big data analysis, a total of 776 Landsat scenes were analyzed that cover a part of the Han River in South Korea. A total of eleven sample datasets was taken at the upstream, mid-stream and downstream along the Han River. This research aimed at analyzing locational variance of reflectance, analyzing seasonal difference, finding long-term changes, and modeling algal amount change. There were distinctive reflectance differences among the downstream, mid-stream and upstream areas. Red, green, blue and near-infrared reflectance values decreased significantly toward the upstream. Results also showed that reflectance values are significantly associated with the seasonal factor. In the case of long-term trends, reflectance values have slightly increased in the downstream, while decreased slightly in the mid-stream and upstream. The modeling of chlorophyll-a and Secchi disk depth imply that water clarity has decreased over time while chlorophyll-a amounts have decreased. The decreasing water clarity seems to be attributed to other reasons than chlorophyll-a.

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