**About the Editors**

#### **Benedetto Nastasi**

Benedetto Nastasi (PhD) is Senior Energy Planner and Assistant Professor at Sapienza University of Rome Previous affiliations include TU Delft University of Technology, TU/e Eindhoven University of Technology, The Netherlands, and International Solar Energy Society and Guglielmo Marconi University, Italy. His work is related to Power-to-What solutions for energy systems design with a specific focus on the built environment. He has developed expertise on hydrogen technologies, energy efficiency, hybrid systems, energy efficiency in buildings, distributed generation, as well as micro and smart grids. He holds a PhD with honors in Energy Systems Planning and Design at Sapienza University of Rome.

#### **Massimiliano Manfren**

Massimiliano Manfren (PhD) is Lecturer in the Sustainable Energy Research Group (SERG), within the Faculty of Engineering and Physical Sciences of the University of Southampton (UK). His previous affiliations include Politecnico di Milano (IT) and University of Bologna (IT). His research focuses on analytics and predictive models for energy system design and operational optimization at multiple scales, from individual users to communities. His research aims to establish a convergence between scientific disciplinary knowledge in energy demand modelling at multiple levels; energy-efficient technologies; and advances in machine learning and operation research techniques, through an integrated use of simulation, optimization, statistics, and data mining on case studies. He holds a PhD in "Programming, Maintenance, and Rehabilitation of Buildings and Urban Systems" from Politecnico di Milano.

#### **Michel Noussan**

Michel Noussan (PhD) is Senior Research Fellow at Fondazione Eni Enrico Mattei (FEEM) Future Energy Research Program and Affiliate Professor of Sustainable Transport at Sciences Po's Paris School of International Affairs (PSIA). His current research activities are focused on the analysis and comparison of different mobility solutions in the framework of decarbonization and digitalization trends of the transport sector. He has developed expertise on energy systems analysis, combined heat and power, district heating, energy efficiency and local energy planning. He was a researcher and university lecturer at Politecnico di Torino in the domain of energy systems analysis, and he has a track record of several publications in international journals and conferences. He holds a PhD in Energy Engineering from Politecnico di Torino.

### *Editorial* **Open Data and Models for Energy and Environment**

**Benedetto Nastasi 1,\* , Massimiliano Manfren <sup>2</sup> and Michel Noussan <sup>3</sup>**


#### **1. Overview of the Articles in This Special Issue**

An increasing number of data sources and models to handle them call for transparency and openness in assessing their goodness and practical use for people. The simplest and most robust tools to collect, process, and analyse data to offer solid data-based evidence for future projections in building and district and regional system planning are of interest. For this purpose, and following the success of the first Special Issue "Open Data and Energy Analytics", the Special Issue "Open Data and Models for Energy and Environment" has been launched, intended for energy engineers and planners. Among a very high number of submissions, 10 articles were selected for acceptance and published.

The first paper by Noussan and Neirotti [1] provides a quantification of the potential influence of different charging strategies on the average emission factor of the electricity supplied to electric vehicles. The next paper by Prina et al. [2] is related to the application of the EPLANOPT model to the Italian energy system, showing the difficulties to meet the Paris Agreement target of limiting the temperature increase to 1.5 ◦C.

The third paper in this special issue, by Neshat et al. [3], presents an optimization framework of a multi-mode wave energy converter to be tested in a small island in the west of Sicily, Italy, in the Mediterranean Sea. Cardone and Gargiulo [4], in the fourth paper of this special issue, describe a semiempirical model of a scroll compressor to predict the power consumption and the mass flow rate by considering leakages and mechanical losses. The next paper, by Amini et al. [5], performs a parametric study on wave energy converter layouts, investigating the distance influence and the effect of rotation regarding significant wave direction in each arrangement compared to the predefined layout. The sixth paper of this special issue, by Chiosa et al. [6], proposes an innovative anomaly detection and diagnosis methodology to automatically detect anomalous energy consumption in buildings, in addition to performing a diagnosis on the sub-loads that are responsible for anomalous patterns. In the next paper, Henrich et al. [7] analyse the impact of energy models in decision making processes for energy transitions in ten municipalities in the Netherlands. In the eight paper, Manfren et al. [8] review the role of energy modelling and analytics for energy transitions in the construction sector. Skeie and Gustavsen [9] investigate the use of geospatial data to improve the level of definition of weather variables used in data-driven building thermal performance characterization. Finally, in the tenth paper, Agostinelli et al. [10] illustrate the use of cyber-physical systems, Internet of things, and machine learning to achieve optimized energy management for a residential district in Rome.

**Author Contributions:** Conceptualization, B.N.; writing—original draft preparation, B.N., M.M. and M.N.; writing—review and editing, B.N., M.M. and M.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Citation:** Nastasi, B.; Manfren, M.; Noussan, M. Open Data and Models for Energy and Environment. *Energies* **2021**, *14*, 4413. https://doi.org/ 10.3390/en14154413

Received: 29 May 2021 Accepted: 15 July 2021 Published: 22 July 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Conflicts of Interest:** The authors declare no conflict of interests.

#### **References**

