# Implementing Smart City Technologies to Inspire Change in Consumer Energy Behaviour
## 3 Smart Meter Implementation in Bucharest Pilot  
A smart meter pilot was developed on the part of the campus of University Politehnica of Bucharest (UPB). At the beginning of the project there was no smart electricity metering infrastructure available on site. The baseline situation was that the energy bill was being issued based on total electricity consumption in the campus, with identification of energy consumption limited to commercial customers (e.g., restaurants, clubs, shops, etc.).  
As it was not possible to install a smart meter per every user (room) due to legacy infrastructure, the solution was based on Smart Low-Cost Advanced Meters (SLAM), single phase meters that were developed within the NobelGrid project [38] and Unbundled Smart Meters (USM), three phase meters which contain a single Smart Meter extension (SMX) connected to a LandisGyr three phase meter. To cover the energy supply of five student buildings, 39 SLAMS and 12 USMs were installed. In every dorm there are three SLAMs or one USM deployed per floor, monitoring a total of almost 2000 students in an aggregated, anonymized way, thus demonstrating the strategy which can be replicated for situations were various technical and financial limitations exist.  
The concept behind the development of the pilot site is based on demonstrating the new patterns of residential consumption, very different from the classical, predefined load curves that are based only on statistical assumptions. These new patterns of consumption can be used in a variety of applications ranging from consumer clustering to demand response algorithms and from load forecasting to contractually binding consumption patterns between energy communities and utilities [39]. In the context of the envisioned solution the information can be used as benchmarking data for the gamification tool and for defining the scenarios behind the series game algorithms.  
Going beyond the possible application of these patterns and trying to identify why they are highly different from the classical and theoretical ones, at least two main possible causes can be identified:  
1. The types of appliances that are currently in use in households are very different from those present not only a couple of decades ago but even a year or two ago. Mostly resistive loads are replaced with electronics and static converter interfaced loads.
2. The new generation of household owners represented by technical university students [40] have other behavioural patterns than their parents and grandparents (e.g., they have jobs with flexible hours, they cook less at home, play more online games, etc.). It is to be mentioned that the presented results do not consider the work from home phenomena more and more present starting in 2020.  
As part of the project, data from the meters in the pilot site were measured continuously from July 2018 until the COVID-19 lockdown in the spring of 2020 that implied the forced move of the students' off-campus.  
Studied applications for these specific meter data included, but were not limited to, evaluation of forms carbon footprint, monitoring power quality, utilising data as input for control algorithms for hybrid microgrids in local energy communities [41] and developing unique indicators of energy usage [9].