A knowledge discovery in databases approach for industrial microgrid planning

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

卷:60

Review

Gamarra, Carlos¹  Guerrero, Josep M.²  Montero, Eduardo¹ 

[1] Univ Burgos, Dept Electromech Engn, Burgos, Spain

[2] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark

关键词: Microgrid planning;    Knowledge discovery in databases;    Energy Management Systems;    Data Mining;    Machine Learning;    Sustainability;   

DOI  :  10.1016/j.rser.2016.01.091

来源: Elsevier

【 摘 要 】

The progressive application of Information and Communication Technologies to industrial processes has increased the amount of data gathered by manufacturing companies during last decades. Nowadays some standardized management systems, such as ISO 50.001 and ISO 14.001, exploit these data in order to minimize the environmental impact of manufacturing processes. At the same time, microgrid architectures are progressively being developed, proving to be suitable for supplying energy to continuous and intensive consumptions, such as manufacturing processes. In the merge of these two tendencies, industrial microgrid development could be considered a step forward towards more sustainable manufacturing processes if planning engineers are capable to design a power supply system, not only focused on historical demand data, but also on manufacturing and environmental data. The challenge is to develop a more sustainable and proactive microgrid which allows identifying, designing and developing energy efficiency strategies at supply, management and energy use levels. In this context, the expansion of Internet of things and Knowledge Discovery in Databases techniques will drive changes in current microgrid planning processes. In this paper, technical literature is reviewed and this innovative approach to microgrid planning is introduced. (C) 2016 Elsevier Ltd. All rights reserved.

【 授权许可】

Free