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The Data Management Plan clarifies the handling of research data during and after the project. It includes data that will be collected, processed or generated during the project, methodology and standards that will be applied, whether data will be shared and how data will be curated and preserved, taking into account all data-related aspects of the project. The document describes: The guiding principles for data management in the project The legal framework constituted by the General Data Protection Directive (GDPR) An overview of what data will be gathered and processed in the project How data will be sotred and processed according to the H2020 FAIR data management principles Resource allocation for making data FAIR Data security aspects ePLANET project is a Coordination and Support Action cofounded by the European Commission through Horizon 2020 program. ePLANET aims to deploy a new clustering governance for energy transition based on a digital framework to share harmonized information, facilitating the adoption of coordinated energy transition actions by the European public sector.

This thesis aims at developing a method that makes use of advanced statistical models to analyze building consumption data and assess energy retrofit impact. The research is focused on tertiary buildings and the models are based on hourly and sub-hourly smart meters data ; It is estimated that about 40% of worldwide energy use occurs in buildings [ 1 ]. Increasing energy efficiency in the building sector has become a priority worldwide and especially in the European Union. It is clear that an immense energy efficien cy potential lies in buildings and it is not properly harnessed. The energy efficiency increa se can be realized through energy retrofitting actions, optimization of the building c ontrol strategy, or through the timely reporting of abnormal energy performance. In this thesis, a framework for the evaluation of the impact of energy retrofitting measures, with a statistic al learning approach, is proposed. The model was developed as part of EDI-Net, a Horizon 2020 pro ject, with the main goal of facilitating energy consumption monitoring in buildings a nd allowing analysis and evaluation of applied energy efficiency measures (EEM). The baseline mod els for the impact evaluation are generated using Generalized Additive Models (GAM), enh anced with auto regressive terms. Three different pilot buildings (one in Spain and two i n the UK) are examined and their savings evaluated through the analysis of hourly smar t meter consumption data and weather data. The results show that it's possible to evaluat e energy savings in tertiary buildings using a data-driven approach, although further w ork is needed, in order to validate and automatize the model.

This thesis aims at developing a method that makes use of advanced statistical models to analyze building consumption data and assess energy retrofit impact. The research is focused on tertiary buildings and the models are based on hourly and sub-hourly smart meters data ; It is estimated that about 40% of worldwide energy use occurs in buildings [ 1 ]. Increasing energy efficiency in the building sector has become a priority worldwide and especially in the European Union. It is clear that an immense energy efficien cy potential lies in buildings and it is not properly harnessed. The energy efficiency increa se can be realized through energy retrofitting actions, optimization of the building c ontrol strategy, or through the timely reporting of abnormal energy performance. In this thesis, a framework for the evaluation of the impact of energy retrofitting measures, with a statistic al learning approach, is proposed. The model was developed as part of EDI-Net, a Horizon 2020 pro ject, with the main goal of facilitating energy consumption monitoring in buildings a nd allowing analysis and evaluation of applied energy efficiency measures (EEM). The baseline mod els for the impact evaluation are generated using Generalized Additive Models (GAM), enh anced with auto regressive terms. Three different pilot buildings (one in Spain and two i n the UK) are examined and their savings evaluated through the analysis of hourly smar t meter consumption data and weather data. The results show that it's possible to evaluat e energy savings in tertiary buildings using a data-driven approach, although further w ork is needed, in order to validate and automatize the model.

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities. ; This research has received funding from the European Union's Horizon 2020 research and innovation programme under the ENTRACK project [Grant Agreement 885395]

A bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. To demonstrate the validity of this methodology, the characterisation of the electricity consumption of the whole province of Lleida, located in northeast Spain, is implemented and tested. The geographical aggregation level considered is the postal code since it is the highest data resolution available through the open data sources used in the research work. The development and the experimental tests are supported by a web application environment formed by interactive user interfaces specifically developed for this purpose. The paper's novelty relies on the application of statistical data methods able to infer the main energy performance characteristics of a large number of urban districts without prior knowledge of their building characteristics and with the use of solely measured data coming from smart meters, cadastre databases and weather forecasting services. A data-driven technique disaggregates electricity consumption in multiple uses (space heating, cooling, holidays and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from this disaggregated energy uses to obtain the energy characterisation of the buildings within a specific area. The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sector. ; This work emanated from research conducted with the fi-nancial support of the European Commission through the H2020project BIGG , grant agreement 957047, and the JRC Expert Con-tractCT-EX2017D306558-102.D.ChemisanathanksICREAfortheICREA Acadèmia. Dr J. Cipriano also thanks the Ministerio deCiencia e Innovación of the Spanish Government for the Juan dela Cierva Incorporación grant