Scenario: Energy Monitoring

In the context of Smart City the energy is a key factor for many aspects: financial, quality of life, reduction of pollution (Data Analytic: Predicting Air Quality), and ranking. The energy is typically consumed for public lighting, residential building, industries, ICT infrastructures, recharging stations of public means (cars and busses) and taxi, etc. In the recent years, a strong push to energy saving in the cities has been observed, moving from fossil combustion to electric powered solutions as e-vehicles, sharing economy, etc. The reduction of traditional vehicles in favor of e-vehicles may lead to reduce the production of NOX, and thus to reduction of pollution, according to the international directives (Data Analytic: Predicting Air Quality). In most cases, the NOX is for the 70% provoked by fossil combustion of transportation means (Scenario: High Resolution Prediction of Environmental Data). Thus a strong reduction of NOX can be obtained only by pushing the entire population to move toward the public means and/or e-vehicles.

This very short note does not pretend to be exhaustive, neither to go in deeps on this argument, but only aims at putting in evidence the kind of data a City may be interested to collect into the data store for strategy, control room and monitoring the whole city and related services. The data can be almost al reconduced to energy consumption in real time, per day, per week and per months.


From the point of view of the data, the cities have to be capable to monitor the energy in the main areas in which they have relevant consumptions and the corresponding trends over time: public light, residential building, ICT infrastructures, recharging stations of public means (cars and busses) and taxi, etc. From these services, the collection of energy consumption data may bring to compute Key Performance Indicators that may be useful for the Decision Makers.


Public lighting is moving towards the usage of LED lamps and thus a strong push towards smart light solutions has been registered. They allow to be finely regulated, so that to avoid the classic on/off approach, but controlling the percentage of light needed on the basis of the actual instensity of the natural lights and contextual conditions (for example, presence of people passing, car passing, the occurence of critical situations). The infrastructure for public lighting, in most cases, may also bring connectivity wi-fi, provide environmental sensors or traffic sensors, etc.


Residential and public buildings may be monitored via the smart meters that are progressively installed in most of the cities to enable the remote reading of the meters and also a deeper monitoring of the consumption over the single day/hour instead of getting a value reporting the global consumption in the month. This feature is particular relevant for energy providers that may be capable to regulate their offer on the basis of the actual/predicted consumption with a higher precision. In certain cases, some residential and/or industrial plants may also produce energy that may be provided to the network. See for example: Scenario: 5G Enabled Water Cleaning Control  (smart city, industry 4.0).  In that case, the energy consumed by each single pump in the water depuration infrastructure is monitored to control the  efficiency. In those cases, predictive models can be activated.


ICT infrastructures are surely a source of energy consumption, while it is strongly limited with respect to the above mentioned. A single ICT infrastructure may have an equivalent consumption of a large public building, for example. So that, the demand of energy monitoring may be very similar. The monitoring of energy consumption is also used to control the service since eventual disfunctions (over consumption, as well as strong reductions) can be the alarm of the inception of a critical event, thus provoking an early warning to the operators. In the Dashbord reported on right, the monitoring of the energy consumed by the DISIT Data center is depicted. It includes 3 air conditioners and a number of UPS devices. In this case, Snap4City has also developed the IOT devices for measuring energy consumption in real time.


Recharging stations in city are rapidly grawing in number and performance. Private city users are installing their own solutions according to the vehicles they are taking, while cities are providing on the streets (in agreement with energy providers) a number of solutions for regular ad fast recharge of vehicles. This activities is performed as incentives to the city users to pass at the e-vehicles, and thus to reduce the emission of NOX. The first consumers of those solutions should be the public means (busses and cars), taxies and the commuters. At the same time the city may stimulate the commuters to use the public transportation provided, providing better solutions (passing from busses to tram and metro, Scenario: Multipurpose User Engagement Tools), improving the public services performing a deeper analysis of the match from mobility demand vs transportation offer (Data Analytic: Analyzing Public Transportation Offer wrt Mobility Demand), etc.


All the collected energy related KPI may be rendered on specific dashboards as those reported in the article making them accessible for the decision makers. The following dashboard belongs to the set of Dashbords of the Smart City Control Room of Florence realized in the context of REPLICATE project. Scenario: Firenze Smart City Control Room.

In this - Energy -  dashboard, it is possible to find the status of the recharging stations and their consumptive energy values, the status of smart light infrastructure lamp by lamp, etc. 

Partners: Comune di Firenze, REPLICATE, UNIFI.


Additional Information and realted scenarious are: