Many a time, one party estimates what the other person should pay, based on what electrical gadgets they process and use.
The misunderstanding is hardly ever resolved, because, every argument, at the very best, hinges on educational conjectures.
There is however some respite by the ingenuity of three Computer Engineering students of Kwame Nkrumah University of Science and Technology.
Hillary Ruby Lani Kisser, Frederick Obeng-Nyarko, Sonia Leninsor Afua Akoto and have designed a smart distribution panel for real-time monitoring of energy consumption of specific gadgets.
Photo (above): [L-R] Hillary Ruby Lani Kisser, Frederick Obeng-Nyarko and Sonia Leninsor Afua Akoto
It also provides electrical protection for each circuit in a common enclosure.
“The traditional distribution panel has no form of interaction between what’s happening in the panel and the homeowner. So whether there’s too much voltage coming in, there’s no way of finding out. Your bulb will burn out and you don’t know what happened,” Hillary Ruby Lani Kisser told Luv FM’s Kwesi Debrah.
She and her final-year colleagues sought to determine the status of the circuit breakers on the panel.
Achieving real-time monitoring of voltage, current values and detecting early electrical faults in electrical distribution system were part of their objectives.
The system consists of current transformer sensors, microcontroller, GSM, server and Mobile application.
“The current transformer sensors measure the energy consumption of loads (bulb) and pass it to the microcontroller, sends to GSM and it’s sent the mobile App which plots a graph to see energy consumed to monitor whatever is happening with gadgets,” Frederick explained.
The values picked up by the sensors are sent to the mobile app to show the breakdown of the energy consumed.
“In addition, it plots a graph to show how energy is consumed and the user gets to see all the information,” Sonia added.
The students are working to remotely control the distribution panel.
The team also wants to apply advanced Artificial Intelligence algorithms and Big Data analysis to optimise energy consumption through what they call efficient demand-side management.