## 1 Introduction
Considering all these elements, the OptEEmAL tool provides four processes into an integrated solution around an interoperability framework to ensure appropriate use and exploitation of data. These processes are:  
* **Data insertion (1). ** Users insert the required data into the tool to perform the evaluation of the baseline and to create the candidate retrofitting alternatives. * **Data integration and baseline calculation (2). ** After the users have inserted the data, the OptEEmAL tool creates the first instance of the data model (baseline) and calculates the values of the relevant indicators to characterise its performance. For this, a set of simulation models are created and processed through simulation tools to retrieve and post-process these indicators which are calculated at district level. * **Scenarios generation and optimisation process (3). ** Once the baseline is calculated, the tool generates a set of candidate retrofitting scenarios based on implementing Energy Conservation Measures to the baseline model. These models are then calculated through the simulation tools integrated into the tool and provide the indicators that characterise their performance. Groups of scenarios are generated and evaluated until the stopping criteria are met. * **Outcomes exportation (4). ** After calculating the best scenarios, these are presented to the users of the tool who can select the most suitable and export all data that has been produced for it. All these four processes together allow for delivering more informed decisions and reducing time and costs of the design process, having also an impact on the subsequent stages of the process. A core element in the design and development of the OptEEmAL tool is on the role and integration of the users and the way of transforming their subjective establishment of preferences into a mathematical problem to drive the optimisation of the retrofitting scenarios. As stated above, one of the novelties of the tool presented is the fact that it tackles the design and optimisation problem at district scale, analysing therefore the inter-building effects. This is done in terms of three main processes: a) indicators are computed and aggregated at district level so that the optimisation allows balancing the district as a whole, b) to ensure that simulations are district-aware, the effects of surrounding district environment is considered, and c) energy systems at district scale are included as part of the catalogue so that it is ensured that the technologies proposed not only optimise the building performance, but also the district performance. The calculation of indicators at the district scale is done by means of aggregation of indicators at lower levels (space, building, etc.) These are calculated with the tools integrated within the OptEEmAL platform and then post-processing and aggregation techniques are implemented to build the district performance indicators which are used for the optimisation approach presented within this paper. With regards to computing and evaluating the inter-building effects, these occur mainly at passive and active level, corresponding with the assessment of the effect that the surroundings have within the evaluated buildings that form the district, and with the integration as candidate ECMs of technologies at district scale respectively. In order to ensure that the effects are appropriately modelled and then calculated, a district neighbour shading (DNS) tool has been deployed which uses an algorithm developed to determine the set of surfaces that block the solar energy from entering the simulated buildings [9]. This tool receives the information coming from the IFC and CityGML models and then, based on input variables as the latitude, longitude, and the identification of the buildings under study, it provides the geometry of the shading surfaces as a XML file [13].