AI4PublicPolicy / Lisbon, Portugal

Energy Management Policies

📍Lisbon, Portugal
About the pilot

The pilot focuses on gathering relevant and valid data sources, such as weather, buildings’ characteristics and energy consumption, and with the aid of AI and machine learning algorithms detect patterns and problems regarding energy efficiency, so that possible data-driven policies can be defined to ensure a more sustainable and efficient environment in the city. Data regarding energy consumption will be gathered using smart meters and IoT devices. Apart from the current energy consumption time interval of 15 minutes, with the implementation of IoT devices, a deeper analysis will be made, to analyse specific types of energy consumption in each building, assuming a breakdown of consumption considering the equipment existing in each delivery point. Data regarding buildings archetypes is gathered with the support from other projects of LIS. With these main data sources, AI models can be implemented to analyse patterns and support policy making on general energy performance improvement. Deep Learning models such as long/short-term memory network (LSTM Network), Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs) can also be used to provide forecast on energy consumption. Clustering algorithms, e.g., k-Means, Hierarchical Cluster Analysis and Expectation Maximization can be used to group clients on similar energy usage.

Pilot Poster
Use Cases
  • Energy performance analysis: This use case will identify problems in the buildings’ energy performance and will perform root cause analysis;
  • Budget planning for energy usage and buildings renovation: This use cases will focus on policy making decisions on budget planning and effective use of budget and public resources towards buildings renovation and energy usage. For instance, policies for the definition of specific financial supporting mechanisms or retrofit schemes will be recommended.
User Stories
No. User Goal Reason
#1 City officer/Energy agency To identify and locate the PV systems installed in the city as well as to know their areas. To understand about the city energy production performance; be able to deduce installed capacity; and infer about the city’s PV adoption curve through time.
#2 City officer/Energy agency/Building(s) owner To know about the city’s potential PV energy production. To be able to monitor PV energy production potential and draw scenarios elucidating about the achievement of city’s defined goals and targets for 2030.
#3 City officer/Energy agency To realize buildings roof insulation capacity (i.e. thermal insulation performance). To understand and infer about energy performance of existing buildings; to assist on city’s investment needs calculation; and (possibly) support definition of potential financial mechanisms.
#4 City officer/Energy agency/Building(s) owner/Real Estate market (?) To know how external variables (e.g. temperature, humidity, solar exposure) and performed interventions influence buildings energy performance. To be able to quantify impacts of performed interventions and better plan future interventions (considering the weather/climate forecast models); to understand how energy consumption varies with meteorological variables through root cause analysis.
#5 City officer/Energy agency To ensure that the city’s building register is updated regarding the type of uses (e.g. residential, services, etc.) at building level. To better understand city’s energy performance and energy consumption by sector of activity, and be able to infer about potential energy demand through time.
#6 City officer/Energy agency To map air temperature variations throughout the city (e.g. thermography images for on a monthly basis). To understand the city thermal performance, locate the main hot spots and infer the reasons through root cause analysis, as well as to elucidate about ‘urban heat island’ effects.
#7 City officer/Energy agency To realize the carbon capture and storage capacity of the city’s green areas. To understand the potential impact of the green areas created in the recent years as well as their influence for the carbon emissions balance.
#8 City officer/Energy agency To assess the non-CO2 greenhouse gases (GHG) emissions in the city. To provide the developed policies, plans with more accurate information, as well as to be able to better monitor defined goal related to GHG inventory.
#9 City officer/Energy agency To be aware of different pollutant’s concentrations in different areas of the city, and be automatically informed when these exceed the defined air quality limit values. To monitor the air quality throughout the city, identify hot spots and inform citizens when air quality limit values are exceeded.
#10 Citizen To know about city’s air quality. To better plan the trip to a specific point in the city or place to exercise
Co-creation Workshops

First co-creation workshop in Lisbon

Discover more about the first co-creation workshop for the Lisbon pilot of AI4PublicPolicy project, organised by Lisboa E-Nova that took place at the City of Lisbon Foundation on December 15th 2021.

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