Data-driven demand-side energy management approaches based on the smart energy network. (November 2019)

• Record Type: Journal Article
• Authors: Pan, Feng; Lin, Guoying; Yang, Yuyao; Zhang, Sijian; Xiao, Jucheng; Fan, Shuai
• Is Part Of: Journal of algorithms & computational technology. Volume 13(2019)
• Publication Date: 2019-11
• Subjects: Smart energy network -- smart energy management -- power utility functions -- HMM; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748302619891611
• Languages: English
• ISSNs: 1748-3018

Abstract

The energy shortage problem cannot be ignored in the development of economics. A demand-side smart energy network is introduced in this paper, which integrates renewable energy resources, energy storage devices, and various types of load into an autonomous distributed architecture. Our approach employs the internet, Internet of Things (IoT), data mining, and other advanced technologies. The network aims to share energy information to realize distributed smart energy management, which can, for example, save energy while ensuring the reliability and quality of electricity for customers. Based on the network, a series of smart energy management theories are proposed to support the smart energy network. The core idea of these theories is to allow the network to teach itself in order to learn from the massive amounts of collected energy data, by using machine learning algorithms. The concept of power utility is proposed to quantitatively assess load energy efficiency. Then, a data-driven consumer energy activity recognition method is proposed based on a hidden Markov model (HMM). A test system is generated using field data from a pilot project in Guangdong Province, China. The energy saving rate in our test is 37.9%, which means that the smart energy network and the proposed algorithms perform well for automatic and intelligent energy efficiency management.