A. Di Piazza

Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy

Abstract The availability of good and reliable rainfall data is fundamental for most hydrological analyses and for the design and management of water resources systems. However, in practice, precipitation records often suffer from missing data values mainly due to malfunctioning of raingauge for specific time periods. This is an important issue in practical hydrology because it affects the continuity of rainfall data and ultimately influences the results of hydrologic studies which use rainfall as input. Many methods to estimate missing rainfall data have been proposed in literature and, among these, most are based on spatial interpolation algorithms. In this paper different spatial interpolation algorithms have been evaluated to produce a reasonably good continuous dataset bridging the gaps in the historical series. The algorithms used are deterministic methods such as inverse distance weighting, simple linear regression, multiple regression, geographically weighted regression and artificial neural networks, and geostatistical models such as ordinary kriging and residual ordinary kriging. In some of these methods, the elevation information, provided by a Digital Elevation Model, has been added to improve estimation of missing data. These algorithms have been applied to the mean annual and monthly rainfall data of Sicily (Italy), measured at 247 raingauges. Optimization of different settings of the various interpolation methods has been carried out using a subset of the available rainfall dataset (modeling set) while the remaining subset (validation set) has been used to compare the results obtained by the different algorithms. Validation results indicate that the univariate methods, neglecting the information of elevation, are characterized by the largest errors, which decrease when the elevation is taken into account. The ordinary kriging of residuals from linear regression between precipitation and elevation, which has provided the best performance at annual and monthly scale, has been used to complete the precipitation monthly time series in Sicily.

A novel EMS for residential microgrids reconciling end-user and utility needs

Among those currently proposed in the technical literature, most Energy Management Systems (EMSs) that are based on the formulation and solution of an optimization problem, can be classified in two categories: some of them solve the problem using Dynamic Programming (DP), which is quite computationally expensive in terms of memory occupation; others, in order to solve the problem using Linear Programming (LP) that has a lower computational cost, introduce a simplification, i.e., they consider positive and negative power flows at bidirectional devices separately, instead of considering the net exchanged power. Furthermore, each currently available EMS is only able to achieve one goal at a time, providing advantages either for the end-user or for the grid manager/administrator. Starting from the above considerations, a novel EMS for residential microgrids is proposed in this paper. It exploits the forecasting of PV generation and load demand profiles by means of suitably chosen and trained neural networks. Furthermore, it is based on solving two different optimization problems during two stages of the algorithm, aiming at reconciling end-user and utility needs. Thanks to a suitable mathematical formulation, it manages to solve the optimization problems using Mixed Integer Linear Programming (MILP), instead of DP. A series of simulations is performed to validate the proposed EMS, whose results are presented and discussed.

Energy Management Systems for effective gap reduction between actual and predicted power in smart homes and buildings

In order to optimize energy efficiency and to achieve cost savings in smart buildings and grid-connected smart homes that include renewable generators and electrical storage systems, Energy Management Systems (EMSs) are today the most up to date solution. Besides achieving these two goals, a suitable design of the EMS can provide a quite deterministic management of power flows, reducing the gap between actual and predicted power due to forecasting errors. On the basis of a previously proposed EMS that allows reducing both the end-users electricity bill and the generation/demand uncertainty impact, this paper proposes a detailed analysis of several factors affecting the EMSs performance. Variations of algorithm strategy parameters, market constraints and size of hardware components have been investigated and the results have been evaluated in terms of reduction of power gap and cash flow. Simulation results obtained in a six-day period for a grid connected smart home with a 3 kWp photovoltaic generator and a battery storage system are presented and some guidelines for proper EMS design have been proposed.

Utility of post mortem computed tomography in clivus fracture diagnosis. Case illustration and literature review

Clivus fractures are usually associated with head blunt trauma due to traffic accident and falls. A 23 - year-old man died immediately after a smash-up while he was stopping on his motorcycle. Post-mortem Computed tomography (PMCT), performed before autopsy, revealed a complex basilar skull base fractures associated with brainstem and cranio-vertebral junction injuries, improving the diagnostic performance of conventional autopsy. Imaging data were re-assessable and PMCT offers the possibility to perform multiplanar and volume rendered reconstructions, increasing forensic medicine knowledge related to traumatic injuries.

A geostatistical approach for identifying the shadowing conditions affecting a PV plant

Geostatistical approaches are commonly used in many disciplines, but they can be also useful also in identifying the operating conditions of a photovoltaic field, especially when solar irradiance hits differently its modules or when partial shading occurs. Despite some applications has been presented in literature, different aspects related to the accuracy of the operating condition identification process have been not yet analyzed into detail. This paper is aimed at discussing the dependence of the process with respect to the parameters of the geostatistical method. Moreover, a method for the prediction of the shadow motion is proposed and verified in simulation. The interest of this work relies in opening the on-line application of the proposed methods, e.g. in the dynamic photovoltaic systems reconfiguration.