Deep learning for power system data analysis
| Autor: | Mocanu, Elena, Nguyen, Phuong H., Gibescu, Madeleine, Arghandeh, R., Zhou, Y. |
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| Přispěvatelé: | Electrical Energy Systems, Cyber-Physical Systems Center Eindhoven |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
020209 energy Stability (learning theory) 02 engineering and technology Machine learning computer.software_genre Unsupervised learning Deep belief network Reinforcement learning 0202 electrical engineering electronic engineering information engineering SDG 7 - Affordable and Clean Energy Restricted Boltzmann machine business.industry Deep learning Supervised learning Energy prediction Transfer learning Smart grid 020201 artificial intelligence & image processing Artificial intelligence business computer SDG 7 – Betaalbare en schone energie |
| Zdroj: | Big data application in power systems, 125-158 STARTPAGE=125;ENDPAGE=158;TITLE=Big data application in power systems |
| Popis: | Chapter Overview Unprecedented high volumes of data are available in the smart grid context, facilitated by the growth of home energy management systems and advanced metering infrastructure. In order to automatically extract knowledge from, and take advantage of this useful information to improve grid operation, recently developed machine learning techniques can be used, in both supervised and unsupervised ways. The proposed chapter will focus on deep learning methods and will be structured as follows: Firstly, as a starting point with respect to the state of the art, the most known deep learning concepts, such as deep belief networks and high-order restricted Boltzmann machine (i.e., conditional restricted Boltzmann machine, factored conditional restricted Boltzmann machine, four-way conditional restricted Boltzmann machine), are presented. Both, their theoretical advantages and limitations are discussed, such as computational requirements, convergence, and stability. Consequently, two applications for building energy prediction using supervised and unsupervised deep learning methods will be presented. The chapter concludes with a glimpse into the future trends highlighting some open questions as well as new possible applications, which are expected to bring benefits toward better planning and operation of the smart grid, by helping customers to adopt energy conserving behaviors and their transition from a passive to an active role. |
| Databáze: | OpenAIRE |
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