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Poster presented during the H2020 "MUSES" project stakeholder workshop ’Embracing opportunities – Ocean Multi-Use Action Plan development workshop’ (Venice - June 27th, 2018). The poster analyses the potential for development of Multi-Use in the Northern-Adriatic coastal waters by combining existing and innovative touristic services with other traditional economic sectors and assets on the area. Drivers, added values, barriers, and impacts of Multi-Use have been identified integrating desk analysis and stakeholders perception. The results show the existence of a good potential for Multi-Use development and Blue-Growth in the area, still limited by barriers, particularly in the field of legislation, administrative processes and stakeholders capacity. Needed actions at local and regional (sub-national) level are highlighted in order to exploit this potential.

Poster presented during the H2020 "MUSES" project stakeholder workshop ’Embracing opportunities – Ocean Multi-Use Action Plan development workshop’ (Venice - June 27th, 2018). The poster analyses the potential for development of Multi-Use in the Northern-Adriatic coastal waters by combining existing and innovative touristic services with other traditional economic sectors and assets on the area. Drivers, added values, barriers, and impacts of Multi-Use have been identified integrating desk analysis and stakeholders perception. The results show the existence of a good potential for Multi-Use development and Blue-Growth in the area, still limited by barriers, particularly in the field of legislation, administrative processes and stakeholders capacity. Needed actions at local and regional (sub-national) level are highlighted in order to exploit this potential.

To continuously assess the performance of a wind turbine (WT), accurate power curve modelling is essential. Various statistical methods have been used to fit power curves to performance measurements; these are broadly classified into parametric and non‐parametric methods. In this study, three advanced non‐parametric approaches, namely: Gaussian Process (GP); Random Forest (RF); and Support Vector Machine (SVM) are assessed for WT power curve modelling. The modelled power curves are constructed using historical WT supervisory control and data acquisition, data obtained from operational three bladed pitch regulated WTs. The modelled power curve fitting performance is then compared using suitable performance, error metrics to identify the most accurate approach. It is found that a power curve based on a GP has the highest fitting accuracy, whereas the SVM approach gives poorer but acceptable results, over a restricted wind speed range. Power curves based on a GP or SVM provide smooth and continuous curves, whereas power curves based on the RF technique are neither smooth nor continuous. This study highlights the strengths and weaknesses of the proposed non‐parametric techniques to construct a robust fault detection algorithm for WTs based on power curves.

To continuously assess the performance of a wind turbine (WT), accurate power curve modelling is essential. Various statistical methods have been used to fit power curves to performance measurements; these are broadly classified into parametric and non‐parametric methods. In this study, three advanced non‐parametric approaches, namely: Gaussian Process (GP); Random Forest (RF); and Support Vector Machine (SVM) are assessed for WT power curve modelling. The modelled power curves are constructed using historical WT supervisory control and data acquisition, data obtained from operational three bladed pitch regulated WTs. The modelled power curve fitting performance is then compared using suitable performance, error metrics to identify the most accurate approach. It is found that a power curve based on a GP has the highest fitting accuracy, whereas the SVM approach gives poorer but acceptable results, over a restricted wind speed range. Power curves based on a GP or SVM provide smooth and continuous curves, whereas power curves based on the RF technique are neither smooth nor continuous. This study highlights the strengths and weaknesses of the proposed non‐parametric techniques to construct a robust fault detection algorithm for WTs based on power curves.

This study investigates the nexus between renewable energy consumption and economic growth in Italy in the 1970-2007 years. The unit root tests results point out that our four variables are integrated of order one. Cointegration analysis reach contradictory results. However, the long-run estimations reveal that, if renewable energy consumption increases by 1%, real GDP decreases by 0.23%. The Toda and Yamamoto approach shows that exists a unidirectional causal flow, running from renewable energy consumption to aggregate income, in line with the "growth hypothesis". Moreover, these results are confirmed by Granger causality tests. While forecasts applications set out that VAR forecasts represent an improvement on simpler forecasts in more than half the comparisons, for each variable. In fact, VAR gives more accurate forecasts, over-performing the mean, RW, and AR methods.

This study investigates the nexus between renewable energy consumption and economic growth in Italy in the 1970-2007 years. The unit root tests results point out that our four variables are integrated of order one. Cointegration analysis reach contradictory results. However, the long-run estimations reveal that, if renewable energy consumption increases by 1%, real GDP decreases by 0.23%. The Toda and Yamamoto approach shows that exists a unidirectional causal flow, running from renewable energy consumption to aggregate income, in line with the "growth hypothesis". Moreover, these results are confirmed by Granger causality tests. While forecasts applications set out that VAR forecasts represent an improvement on simpler forecasts in more than half the comparisons, for each variable. In fact, VAR gives more accurate forecasts, over-performing the mean, RW, and AR methods.

High Concentrator Photovoltaic Thermal (HCPV/T) systems produce both electrical and thermal energy and they are efficient in areas with high Direct Normal Irradiance (DNI). This paper estimates the lifecycle environmental impact of the HCPV/T 2000x system for both electrical and thermal functionalities. Process-based attributional method following the guidelines and framework of ISO 14044/40 was used to conduct the Life Cycle Assessment (LCA). The midpoint and endpoint impact categories were studied. It was found that the main hotspots are the production of the thermal energy system contributing with 50% and 55%, respectively, followed by the production of the tracking system with 29% and 32% and the operation and maintenance with 13% and 7%. The main contributor to the lifecycle environmental impact category indicators was found to be the raw materials acquisition/production and manufacturing of the thermal energy and tracking systems. The results indicate that the lifecycle environmental impact of the HCPV/T 2000x system is lower compared to fuel-based Combined Heat and Power (CHP) and non-Renewable Energy Sources (non-RES) systems.