Coordinated by Lisboa E-Nova – Agência Municipal de Energia e Ambiente.
Coordinated by Lisboa E-Nova – Agência Municipal de Energia e Ambiente.
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.
|Use Case Name||Renewables (PV) potential and performance analysis|
|Summary||This use case will identify existing assets and assess the potential renewables (PV) production.|
|Description||This use case aims to identify the PV systems existing in the city as well as create methods to assess and determine accurately their production potential. In a first stage, the goal will be to identify and locate the PV systems installed in the city in order to understand about the current energy production performance in the city and infer about the city’s PV adoption curve through time. These analytics should be made by using different available datasets, including orthophotomaps, satellite images, and others available, as well as those provided by existing platforms (e.g., Solis platform).
In a second stage, this use case should provide users with the ability to monitor PV energy production potential, ideally by defining time series and forecast scenarios (e.g., on “day-ahead”; “month ahead”; “quarter ahead”, etc.). This aims to enable the definition of scenarios elucidating about the achievement of city’s defined goals and targets for 2030, as well as to inform building(s) owner(s) and citizens about the potential PV production of their buildings, as well as to provide with estimates about potential cost savings.
|Keywords||Energy performance, renewables, PV production and forecast.|
|Use Case Name||Buildings Energy Performance Analysis|
|Summary||This use case will identify problems in the buildings’ energy performance and will perform root cause analysis.|
|Description||This use case aims to provide user with the ability to better understand about the buildings’ energy performance. Considering the data availability, the focus should be at defining buildings insulation capacity at the roof level (i.e., thermal insulation performance) and to assess how external variables (e.g. temperature, humidity, solar exposure) influence buildings energy performance/consumption. The goals are manyfold and include: understanding about energy performance of existing buildings, that will drive the analysis on city’s investment needs, and (possibly) support definition of potential financial mechanisms (UC#3); to understand how energy consumption varies with meteorological variables through root cause analysis; and to better understand about the impacts of different energy related interventions performed in the buildings (monitoring analysis), which can be used to better plan future interventions.|
|Keywords||Buildings, Energy performance, …|
|Use Case Name||Budget Planning for Energy Usage and Buildings Renovation|
|Summary||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.|
|Description||This use case is partially a consequence of UC#2. Its goal is to provide the user with the ability to better estimate about investment needs regarding the city built environment and support the definition of priorities for potential local financial mechanisms aimed at increasing buildings energy efficiency and city’s overall energy performance.|
|Keywords||Urban thermal variations, Urban heat island, urban air quality monitoring|
|As a «type of user», …
Identify the costumer job(s) to which this user story relates.
|… I want «some goal» …
Describe the intended goal that the user expects to be fulfilled.
|… so that «some reason».
Identify the reason(s) to which this user story relates.
|US38||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.|
|US39||City officer/Energy agency/Building(s) owner(s)||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.|
|US40||City officer/Energy agency||To realize buildings roof insulation capacity (i.e. thermal insulation performance).
Identifying problems and cause analysis
|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.|
|US41||City officer/Energy agency||Analyse PV datasets from diferent data souces (e.g. orthophotomaps, satellite images, and others available)||So I can have a wider range of datasets and that can help me analyse the potential of renewables (PV).|
|US42||Building(s) owner(s) and citizens||To know the potential PV energy production of the building(s) owner(s) and citizens||So i can provide estimates about potential cost savings|
|US43||City officer/Energy agency/Building(s) owner/Real Estate market||To know how external variables (e.g. temperature, humidity, solar exposure) influence buildings energy performance (roof level).||To understand how energy consumption varies with meteorological variables through root cause analysis.|
|US44||City officer/Energy agency/Building(s) owner/Real Estate market||To know how the impacts of different energy related interventions performed in the buildings||To be able to quantify impacts of performed interventions and better plan future interventions (considering the weather/climate forecast models);|
|US45||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.|
|US46||City officer/Energy agency||To define priorities with regard to the priorities for energy related buildings interventions.||To better define potential local financial mechanisms and, ultimately, ensure that impacts are aligned with goals defined for the city.|
|US47||City officer?||Estimate about investment needs regarding the city’s built environment||So i can make decisions about budget planning|
|US48||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.|
|US49||City officer/Energy agency||To be aware (visual way) 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.|
|US50||Citizen||To know about city’s temperature variations and air quality.||To better plan the trip to a specific point in the city or place to exercise.|
|US51||City officer||Correlate the data of the networks of sensors installed in the city with other information source||So with more data i can a better understand the city thermal and air quality performance|
|US52||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.|
|US53||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.|