Name
Abstract | ||
|---|---|---|
Two the largest man-made large-scale systems were created and expanded in the last century: electrical power system and world-wide computer networks, the Internet. Entering the new century, both systems are facing enormous challenges. On the one hand, Smart Grids - modernization of electrical power systems to deal with energy shortage and environment issues - are being developed in accelerating pace. On the other hand, human beings are also facing with unprecedented challenges from collecting, storing, transferring, mining, processing and visualizing massive data: Big data. The advent of a smart grid - the overlay of advanced sensing, communications, and controls on the electric network - is transforming utilities and other power sector players into IT companies. As information technologies are embedded across the entire system - from power plants through transmission lines, substations, distribution circuits, meters, and every device in industrial and residential users. Utilities' operational and information models will increasingly resemble those of telecom, Internet, or even financial trading companies. This will require a fundamentally new approach to interoperability, speed, and managing and making sense of vast new floods of data. Therefore, this paper is to follow this route to address Big data in future power systems. Specifically, in this paper, we report our initial work on predicting wind farm outputs based on historical wind speed data. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/IConAC.2015.7313934 | 2015 21st International Conference on Automation and Computing (ICAC) |
Keywords | Field | DocType |
Big Data,Smart grid,Wind farm output prediction | Electric power,Telecommunications,Smart grid,Information technology,Interoperability,Electric power system,Control engineering,Electric power transmission,Engineering,Big data,Electrical engineering,The Internet | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| E. C. Eze | 1 | 0 | 0.34 |
| Tai-Cheng Yang | 2 | 288 | 19.73 |
| Chris R. Chatwin | 3 | 129 | 13.82 |
| Dong Yue | 4 | 3320 | 214.77 |
| hongnian | 5 | 391 | 46.50 |