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Promoting energy efficiency is a key element of the strategic commitment of the European Community. Prominent among the binding measures established by the 2012 Energy Efficiency Directive to further this vision is the requirement for large companies to conduct energy audits every four years. After receiving the second cycle of energy audit reports in December 2019, a new description of the energy situation of Italian companies was made available. This presented the previously inaccessible possibility of comparing the two situations reported in 2015 and 2019 to assess the development of energy efficiency practices in organizations subject to the legislative obligation of energy audits in the country. To this end, in collaboration with the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), a project was initiated with the aim of developing the tools and methodologies necessary to assess in more detail the evolution that has occurred in the four years since 2015. In this paper, the findings of the analysis conducted on a significant sample of companies in Italy are presented. Through the design of a maturity model to assess the degree of progress achieved in a company's energy management, the results of the two situations were analyzed. The analysis was deepened by assessing the progress achieved in different aspects of Energy Management: "Strategic Approach", "Awareness, Competence, and Knowledge", "Methodological Approach", "Organizational Structure", "Energy Performance Management and Information System", and "Best Practices". Furthermore, the observed variations were statistically tested using a pairwise t-test to make statistical inferences about the maturity of the total population of Italian enterprises under legislative obligation. The results have shown an increase in overall energy management maturity in each maturity dimension. [ABSTRACT FROM AUTHOR]

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20–30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm. [ABSTRACT FROM AUTHOR]