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Establish the AI infrastructure for analysis of the electricity data (3/7)
Approved Pending  +
DueDate 12 May 2022  +
Keywords Fog computing  + , Edge computing  + , Privacy  + , Services  + , Smart Home  +
Methods The tools and methods in this thesis are bThe tools and methods in this thesis are based on * A set of scenario, describing the challenges * A list of requirements being extracted from the scenarios * A description and evaluation of technologies and tools being candidates for solutions * A functional architecture/description of the envisaged system * An implementation of the core concepts * A demonstration of the solution * An evaluation of the solution, including a critical review of the descisions taken earlier * Conclusions * Referencess taken earlier * Conclusions * References  +
Objective Future Homes have the opportunity to be adFuture Homes have the opportunity to be adaptive to the quality of the electricity grid. By monitoring the energy consumption on fuse-level every 10 seconds, one can generate energy profiles of private homes. Current regulations suggest a reading of power values 1/hour, or in maximum every 15 min. Given a high-frequency reading of power consumption (1/10s, 1/min) might give an opportunity to introduce new services. Given the size of the work, several theses can be performed on the topic: # Establish the low-power monitoring infrastructure in the home (Raspberry Pi/Arduino and ModBus to read from Power Taggs) # Create the public source database for energy consumption, combining high-resolution energy data with 3rd party information such as weather data # Establish the AI infrastructure for analysis of the electricity data # Monitoring the home energy distribution - Feature extraction through AI # Potential of contributions from private homes to energy stability # Wireless control systems for home appliances to support the energy transition # Security and privacy assessment of wireless in-house control systems for energy appliances The assessment will be based on high-frequency consumption data from actual smart meters and power taggs by fuse level, as well as modelling of services.e level, as well as modelling of services.  +
Pre-Knowledge This thesis includes a reasonable amount of programming. The envisaged thesis involved security and semantics.  +
Schedule The envisaged time schedule (for a long thThe envisaged time schedule (for a long thesis/60 ECTS) is: :T0 0 starting month, T0+m denotes the month where the contribution to a certain chapter shalle be finalized :T0+2 months: create an initial page describing the scenario :T0+3: Provide a list of technologies which you think are necessary for the thesis :T0+4: Establish the table of content (TOC) of the envisaged thesis. Each section shall contain 3-10 keywords describing the content of that section :T0+7: Provide a draft of section 2 (scenario) and 3 (technologies) :T0+10: Establish a draft on what to implement/architecture :T0+11: Set-up an implementation, testing and evaluation plan :T0+15: Evaluate your solution based on a set of parameters, keep in mind <i>there is no such thing as a free lunch</i> :T0+17: Deliver the thesislunch</i> :T0+17: Deliver the thesis  +
Supervisor Josef Noll  + , György Kálmán  +
ThesisStatus Planned  +
Titel Establish the AI infrastructure for analysis of the electricity data (thesis #3/7)  +
User N.n.  +
Creation dateThis property is a special property in this wiki. 1 June 2021 14:47:32  +
Categories Thesis  +
Modification dateThis property is a special property in this wiki. 1 June 2021 14:47:32  +
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