Geodaysit 2023

In cities, the building sector is the main responsible for energy consumption and carbon dioxide emissions. However, there is a high potential for energy saving by renovating the buildings themselves and the decision-making process to primarily focus on them in the development of smart cities. Several policies have been drafted to set a path towards the mitigation of such impacts, decarbonising the energy supply and reducing the total energy demand. Nevertheless, from a smart city-oriented perspective, it is crucial to elaborate tools to support the choices of policymakers and make citizens aware. Remotely sensed data can be used for assessing the current state, thus simulating possible improvements. Existing literature shows extensive use of infrared thermography for assessing discrete buildings, while little has been done on a district or urban scale.

In this contribution, we present the potential of Aerial Infrared Thermography and LiDAR point clouds for defining energy uses and the potential photovoltaic production. First, AIT is used for energy classification, a key parameter for estimating the current energy demand. Then, two alternative retrofitting scenarios – proposing an improvement by two classes and an upgrade of the whole building stock to meet the highest standards – are compared in terms of primary energy savings and prevented emissions. Options are taken into account also considering the energy supply option, with the possibility of installing photovoltaic panels to power heating pumps as an alternative to traditional heating methods, i.e. district heating and natural gas boilers.

In addition to Infrared thermography, aerial LiDAR point clouds are also key data for planning and managing the energy resources in cities. The efficiency of solar panels primarily depends on the incidence angle of the radiation on the panels and, therefore, proper planning is crucial for the installation and setup of solar plants. One of the possible applications of LiDAR point clouds for the energy sector is to support this phase to maximise efficiency. Thanks to the 3D classified LiDAR point clouds, it is possible to extract the buildings with precise restitution of the pitches, their dimension and orientation, then categorising them into planar/flat, slant or dome types in order to estimate the angle of incidence of sunlight radiations and to better assess the maximum solar potential. In this way, an accurate data sheet for each building can be drafted, reporting precise data on theoretical production and usable surface.

Future developments are related to the development of three-dimensional energy models, to be updated regularly, able to describe precisely the current situation and simulate alternative scenarios. The state of the art smart city digital twins can be also employed for the purpose of urban energy management and to capture and understand the urban energy complexities with respect to time. The concept of energy community can be also introduced at a local level where neighbourhoods generate and share the energy generated from renewable sources.