David Duijnmayer: ‘Roll-out of smart meters could be done a bit smarter’
AMSTERDAM (Energeia) – SynerScope, the software company that specializes in distilling knowledge from large amounts of data, is helping a network operator with the large-scale rollout of the smart meter. By linking different types of data, SynerScope can better and faster predict what is currently installed behind the front door. This way, the network operator can send the right technician with the right equipment.
SynerScope, around since 2011, is new to the energy sector. Until now, the company was mainly active in the financial sector and for insurance companies. Via Eindhoven University of Technology (TUE), where SynerScope originated, the energy network operators came to the attention of the software company specializing in big data. “The ‘big’ in big data is actually not that interesting,” says solutions engineer Thomas Ploeger of SynerScope. “We are particularly interested in issues for which different types of data must be linked. Even then there are still plenty of challenges for the network operators.”
The issue that SynerScope helped solve for this network operator is: how can you roll out the smart meter as smartly as possible? If a plan is made to offer the smart meter in a certain area, it is useful to know what a grid operator will find behind the front door: what kind of meter is there now, what kind of cabinet is around it, what kind of fuses are used and is there possibly asbestos present? “To answer those questions, a series of manual checks is still needed,” says Ploeger. “That takes a lot of time and is error prone.”
SynerScope will collect the information of interest faster and better by linking all the data that was first checked manually. In addition, photos taken by mechanics are used. These are clustered with the help of machine learning: photos showing the same equipment are placed in the same group, so that it is quickly visible whether there are many different meters in a certain area or whether there is great uniformity. “What we do is bring data together to optimize work preparation,” Ploeger summarizes.
Making a work schedule with the correct information, prevents any errors. “Specific training is required when removing certain fuses, and not all mechanics are in possession of that specific training”, Ploeger gives as an example. “It sometimes happens that upon arrival a technician is not allowed to do anything because he does not have the right papers. Good information also helps to plan the required time, so you know how many addresses you can schedule in one day.” According to Ploeger, a first test – in which the SynerScope method was compared with the results of the old, manual method – showed that 30% fewer errors were made.
Source: David Duijnmayer | firstname.lastname@example.org | June 15th, 2017