Sensor-based ambient intelligence for optimal energy efficiency

Purpose – This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation. Design/methodology/approach – 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/opti...

[1]  David Sanders,et al.  A prototype ambient intelligence sensor network based on a computer and radio transceiver , 2007 .

[2]  David Sanders,et al.  Comparing speed to complete progressively more difficult mobile robot paths between human tele‐operators and humans with sensor‐systems to assist , 2009 .

[3]  David Sanders Recognizing shipbuilding parts using artificial neural networks and Fourier descriptors , 2009 .

[4]  David A. Sanders,et al.  Expert system to interpret hand tremor and provide joystick position signals for powered wheelchairs with ultrasonic sensor systems , 2011, Ind. Robot.

[5]  Giles Tewkesbury,et al.  An expert system for automatic design‐for‐assembly , 2009 .

[6]  David Sanders Introducing AI into MEMS can lead us to brain-computer interfaces and super-human intelligence , 2009 .

[7]  David A. Sanders,et al.  Inferring Learning Style From the Way Students Interact With a Computer User Interface and the WWW , 2010, IEEE Transactions on Education.

[8]  Djamel Azzi,et al.  A self-healing mobile wireless sensor network using predictive reasoning , 2008 .

[9]  Uwe Kirchhoff,et al.  Ambient intelligence technologies for industrial working environments in manufacturing SMEs , 2008, 2008 IEEE International Technology Management Conference (ICE).

[10]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[11]  Honghai Liu,et al.  Energy efficiency based on ambient intelligence , 2008 .

[12]  David A. Sanders,et al.  Analysis of successes and failures with a tele-operated mobile robot in various modes of operation , 2012, Robotica.

[13]  David Sanders Controlling the direction of "walkie" type forklifts and pallet jacks on sloping ground , 2008 .

[14]  György Kovács,et al.  Ambient intelligence as enabling technology for modern business paradigms , 2007 .

[15]  David Sanders,et al.  AI tools for use in assembly automation and some examples of recent applications , 2013 .

[16]  David Sanders,et al.  Analysis of the effects of time delays on the teleoperation of a mobile robot in various modes of operation , 2009, Ind. Robot.

[17]  David Sanders Ambient-intelligence, rapid-prototyping and where real people might fit into factories of the future , 2009 .

[18]  Gian Luca Foresti,et al.  Knowledge representation for ambient security , 2007, Expert Syst. J. Knowl. Eng..

[19]  David Robinson,et al.  Simple expert systems to improve an ultrasonic sensor‐system for a tele‐operated mobile‐robot , 2011 .

[20]  Alexander Gegov,et al.  Improving automatic robotic welding in shipbuilding through the introduction of a corner-finding algorithm to help recognise shipbuilding parts , 2012 .

[21]  David Sanders,et al.  Comparing ability to complete simple tele-operated rescue or maintenance mobile-robot tasks with and without a sensor system , 2010 .

[22]  Jasper Graham-Jones,et al.  Simple rules to modify pre-planned paths and improve gross robot motions associated with pick & place assembly tasks , 2011 .

[23]  Carl T.F. Ross,et al.  A robotic welding system using image processing techniques and a CAD model to provide information to a multi-intelligent decision module , 2010 .