• Energy Research

Among the objectives of the Sustainable Development Goals by United Nations, "Affordable and Clean Energy" aims at ensuring access to affordable, reliable, sustainable and modern energy for all. However, in Europe there is not a precise understanding of the unleashed potential that could be achieved through the exploitation of solar and wind resources. This study presents an application to retrieve spatial explicit estimates of Direct Normal Irradiance (DNI) through the use of data from geo-stationary satellites. The energetic demand of large metropolitan areas in Europe is then retrieved and compared with the potential production of energy for domestic use through solar panels. Results of this comparison are presented based on the assumption that only the 1 % of the built up area could be covered with solar panels, and hence devoted to energy production. Outcomes suggest that even such a little coverage, if spread systematically over urban areas can in most of the cases satisfy urban population domestic needs.

Abstract The inestimable value of soil is exemplarily summarized in the definition provided by the European Union (2006), which considers it as "the upper layer of the earth's crust, formed by mineral particles, organic matter, water, air and living organisms". The importance of soil protection is now universally recognized, but despite a lot of debates and principle’s enunciation, in the last decades soil was consumed at a rate of 8 m2 per second. The aim of this study is to propose a model which, on one side, is able to measure variations occurred in land use, and, therefore, to determine soil consumption, and, on the other side, is capable to predict future changes. Specifically, a simulation model has been proposed based on two methods: Joint information uncertainty and Weights of Evidence in order to analyse and predict new built-up areas. The proposed model has been applied to Pisticci Municipality in Basilicata region (Southern Italy). This area is a significant example, because of its high landscape value and, at the same time, of a lot of developing pressure due to touristic activities along the coastal zone.

AbstractWith wind power providing an increasing amount of electricity worldwide, the quantification of its spatio-temporal variations and the related uncertainty is crucial for energy planners and policy-makers. Here, we propose a methodological framework which (1) uses machine learning to reconstruct a spatio-temporal field of wind speed on a regular grid from spatially irregularly distributed measurements and (2) transforms the wind speed to wind power estimates. Estimates of both model and prediction uncertainties, and of their propagation after transforming wind speed to power, are provided without any assumptions on data distributions. The methodology is applied to study hourly wind power potential on a grid of$$250\times 250$$250×250 m$$^{2}$$2for turbines of 100 m hub height in Switzerland, generating the first dataset of its type for the country. We show that the average annual power generation per turbine is 4.4 GWh. Results suggest that around 12,000 wind turbines could be installed on all 19,617 km$$^{2}$$2of available area in Switzerland resulting in a maximum technical wind potential of 53 TWh. To achieve the Swiss expansion goals of wind power for 2050, around 1000 turbines would be sufficient, corresponding to only 8% of the maximum estimated potential.

Landscape preservation in Italy is a major issue in national cultural heritage conservation policies. Urban settlements growth is among the most threatening factors for the correct landscape preservation. Such phenomenon may result in corrupting the correct landscape-system functioning, particularly when the development occurs without precise planning prescriptions. Land-use/cover evolution dynamic is a subject widely and thoroughly investigated, especially concerning consumption of natural and other lands due to anthropogenic activities. This paper focuses on a region in southern Italy, where soil consumption is known to represent a urging matter of concern. However, although the negative impacts of soil consumption are well known, to our knowledge there are no case studies presenting a precise quantitative assessment of the intensity of such phenomenon for the region of interest. Furthermore, this study aims at forecasting the development of urban settlements through the application of the cellular automata model SLEUTH; the case study concerns the Municipality of Altamura (Apulia region, Italy). Results highlight how current landscape preservation instruments alone cannot ensure a reduction in soil consumption phenomenon and how urban areas expansion is incompatible with a correct landscape conservation in the study area.

Land take is one of the most studied phenomena in land use science. The increased attention to the issue of urban growth from both scientists and decision makers is justified by the dramatic negative effects on land use caused by anthropogenic activities. Within this context, researchers have developed and explored several models to forecast land use changes, some of which establish excellent scenario-based predictions of urban growth. However, there is still a lack of operative and user-friendly tools to be integrated into standard urban planning procedures. This paper explores the features of the recently published model FUTure Urban-Regional Environment Simulation integrated into the GRASSGIS environment, which generates urban growth simulation based on a plethora of driving variables. Specifically, the model was applied to the case study of urbanization in the Italian national territory. Hence, the aim of this work is to analyze the importance of population dynamics within the process of urban growth. A simulation of urban growth up to the year 2035 was performed. Results show that, despite the importance given to demographic aspects when defining urban policies over the last several decades, additional factors need to be considered during planning processes to overcome the housing issues currently experienced in Italy.

When using the provided data, please cite the following article: Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859. Summary: This dataset contains an estimation of the average yearly wind speed and of the wind power potential for Switzerland, at a spatial resolution of 250 x 250 meters and over the period from 2008 to 2017. Wind speed data are obtained by modelling data collected at an hourly frequency on a set of up to 208 monitoring stations over the country. The data are then interpolated using a spatio-temporal machine learning model, allowing the estimation of wind speed and its uncertainty at unsampled locations. Then, the modelled spatio-temporal wind speed field is used to estimate the wind power. This is computed based on the characteristic parameters of an Enercon E-101 wind turbine at 100 meters hub height. The latter indicates the distance from the turbine platform to the rotor of an installed wind turbine, showing how high the turbine stands above the ground without considering the length of the turbine blades. The hourly estimations of wind speed are then averaged over each of the ten years studied, for each 250 x 250 spatial location, while wind power data are summed over each year for each spatial unit. Advantages and limitations of the proposed method are discussed in Amato et al. (2021). Data description: The hourly estimation of wind speed and power for Switzerland from 2008 to 2017 are available under request. Here we share the annual values. For both wind speed and power, the data are available over 660697 spatial units of 250 x 250 meters each, covering the entire Swiss territory. Check details in the file Data_description.pdf. Data are provided in the pickle format, see https://docs.python.org/3/library/pickle.html#module-pickle. {"references": ["Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859."]}