Dominant Data Set Selection Algorithms for Electricity Consumption Time-Series Data Analysis Based on Affine Transformation

Abstract

The explosive growth of time-series data, the scale of time-series data (TSD) suggests that the scale and capability of many Internet of Things (IoT)-based applications has already been exceeded. Moreover, redundancy persists in TSD due to correlation between information acquired via different sources. In this paper, we propose a cohort of dominant dataset selection algorithms for electricity consumption time-series data with focus on discriminating the dominant dataset that is small dataset but capable of representing the kernel information carried by time-series data with an arbitrarily small error rate less than ε. Furthermore, we prove that the selection problem of the minimum dominant dataset is an NP-complete problem. The affine transformation model is introduced to define as the linear correlation relationship between time-series data objects. Our proposed framework consists of the scanning selection algorithm with O(n 3 ) time complexity and the greedy selection algorithm with O(n 4 ) time complexity, which are respectively proposed to select the dominant dataset based on the linear correlation distance between timeseries data objects. The proposed algorithms are evaluated on the real electricity consumption data Harbin city in China. The experimental results show that the proposed algorithms not only reduce the size of extracted kernel dataset but also ensure the time-series data integrity in term of accuracy and efficiency.

Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption

“…In the broader context of the techniques used for electricity consumption data driven by explosive growth of time-series data and the capability of the methods there are interesting attempts which propose a cohort of dominant data set selection algorithms for electricity consumption time series with a focus on discriminating the dominant data set that is a small data set but capable of representing the key information carried by time series with an arbitrarily small error rate [ 44 ].…”

Edge Computing: Applications, State-of-the-Art and Challenges

“…Existing research generally divides the architecture of edge computing from the central network to the edge of the network into three layers: the cloud computing layer, the edge computing layer, and the ending layer, as shown in Figure 1 [5][6][7]. Different layers are generally divided according to their computing and storage capabilities.…”

“…In this section, we will introduce several emerging application scenarios based on the edge computing framework design, some of which are discussed in the European Telecommunications Standards Institute (ETSI) white paper, such as video analytics and big mobile data. There are also review papers [4,5] that describe vehicle interconnection, healthcare, intelligent building control, ocean monitoring, and the combination of wireless sensor and actuator networks with edge computing.…”

“…At present, edge computing has received extensive attention from all walks of life, and the results are blooming in many application scenarios; however, there are still many problems in the practical application of edge computing [5]. In this article, we analyze several of the main issues, including optimizing edge computing performance, security, interoperability, and intelligent edge operations management services.…”

“…In the edge computing model, the edge device has the processing capability of performing calculation and data analysis, and migrates some or all of the computing tasks performed by the original cloud computing model to the network edge device, reducing the computing load of the cloud server, slowing down the pressure of the network bandwidth, and improving the processing efficiency of data in the era of Internet of Everything. Edge computing is not to replace the cloud, but to complement the cloud, providing a better computing platform for related technologies such as mobile computing and the Internet of Things [5].…”