Open AccessIntegrating Microsoft IoT, machine learning in a large-scale power meter reading
Author(s) -
Liviu Pascu,
Attila Simó,
Alin Vernica
Publication year - 2019
Publication title -
international journal of new trends in social sciences
Language(s) - English
Resource type - Journals
ISSN - 2547-8842
DOI - 10.18844/ijntss.v3i1.3815
Subject(s) - automatic meter reading , computer science , the internet , smart meter , field (mathematics) , reading (process) , operator (biology) , metre , password , scale (ratio) , electricity meter , internet of things , multimedia , artificial intelligence , power (physics) , world wide web , computer security , electricity , engineering , electrical engineering , telecommunications , mathematics , repressor , law , chemistry , biochemistry , quantum mechanics , political science , transcription factor , wireless , physics , astronomy , pure mathematics , gene
Due to fast technological progress in the power engineering field, the need of new information/communication technologies is more and more underlined. e-Learning has become a viable alternative to traditional teaching/learning techniques, adopted especially due to the advantages offered by the possibility of continuous training. This paper presents a Microsoft internet of things platform for a very large-scale smart power meter reading, used not only for training operative staff of the distribution network operator but also to help end users to control electrical energy that they consume. The strength of this platform for the distribution network operator is that the read data can be used for energy forecast, which is very useful for the future energy consumption optimisation. The platform can be reached through the Internet using a user name and password. A comparison between the results provided by classical teaching/learning methods and the ones achieved using this platform is presented.
Keywords: Machine learning, internet of things (IoT), training.