Sensor‐based ambient intelligence for optimal energy efficiency. Issue 2 (2014)

• Record Type: Journal Article
• Authors: David Robinson; David Sanders; Ebrahim Mazharsolook
• Is Part Of: Sensor review. Volume 34:Issue 2(2014)
• Page Start: 170
• Page End: 181
• Publication Date: 2014
• Subjects: Sensor systems -- Periodicals; Detectors -- Industrial applications -- Periodicals; Engineering instruments -- Periodicals; 681.2
• DOI: 10.1108/SR-10-2012-667
• Languages: English
• ISSNs: 0260-2288

Abstract

<abstract> <title> <x xml:space="preserve"> Abstract </x> </title> <p> <bold>Purpose</bold> – This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation. <bold>Design/methodology/approach</bold> – A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add‐on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment‐ambient and process ambient sources and processed within a service‐oriented‐architecture‐based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition‐based energy consumption warning, online diagnostics of energy‐related problems, support to manufacturing process lines installation and ramp‐up phase, and continuous improvement/optimisation of energy efficiency. <bold>Findings</bold> – An innovative and intelligent solution for energy efficiency optimisation is demonstrated in two typical manufacturing companies, within one case study. Energy efficiency is improved and the novel approach using AmI with KM technologies is shown to work well as an add‐on to currently used energy management systems. <bold>Research limitations/implications</bold> – The decision support systems are only at the prototype stage. These systems improved on existing energy management systems. The system functionalities have only been trialled in two manufacturing companies (the one case study is described). <bold>Practical implications</bold> – A decision support system has been created as an innovative add‐on to currently used energy management systems and energy efficiency software services are developed as the front end of the system. Energy efficiency is improved. <bold>Originality/value</bold> – For the first time, research work has moved into industry to optimise energy efficiency using AmI, extended data sets and KM technologies. An AmI monitoring system for energy consumption is presented that is intended for use in manufacturing companies to provide comprehensive information about energy use, and knowledge‐based support for improvements in energy efficiency. The services interactively provide suggestions for appropriate actions for energy problem elimination and energy efficiency increase. The system functionalities were trialled in two typical manufacturing companies, within one case study described in the paper.</p> </abstract>