Energy management and optimisation

Lisbon, Portugal | Led by Lisboa E-Nova (LIS)


Local governments all over the world are formally signing the Covenant of Mayors for Climate and Energy commiting to adopt an integrated approach to climate change mitigation and adaptation, whereas most of them set as vision carbon neutrality by 2050, in alignment with the Paris goals. Signatories are required to develop, within the first two years of adhesion, a Sustainable Energy and Climate Action Plan (SECAP) with the aims of cutting CO2 emissions by at least 40% by 2030 and increasing resilience to climate change. However, designing, implementing and monitoring a SECAP is a complex task. The mitigation goals and strategies required for promoting energy efficiency in end-use sectors are not only difficult to establish, but also to implement and monitor. Therefore, cities urge for available tools and methods that support them in these difficult tasks. The city of Lisbon is currently faced with similar challenges and is considering data-driven solutions for the elicitation of evidence-based policies. With growing data availability, new and different approaches on standard decision-making process can lead to more accurate solutions. In Lisbon, buildings’ energy efficiency is a problem that can be tackled with a new framework.

Pilot Scope

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 are 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 Cases

Renewables (PV) potential and performance analysis


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.

Value Proposition


Energy performance, renewables, PV production and forecast.

Buildings energy performance analysis


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 manifold 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.

Value Proposition


Buildings, energy performance

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. 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.


Urban thermal variations,uUrban heat island, urban air quality monitoring

Lisbon Pilot News & Activities

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