No Shoneen: Gaelic Games and the Athletic Hero in the Irish Periodical Press

Overview of my PhD thesi

User Behavior Clustering Based Method for EV Charging Forecast

The increasing adoption of electric vehicles poses new problems for the electrical distribution network. For this reason, proper electric vehicle forecasting will be of fundamental importance for a predictive energy management system, which could greatly help the operation of the grid. This paper proposes a comprehensive novel methodology to forecast single charging sessions of electric vehicle and the resulting cumulative energy forecast of the charging infrastructure. Historical charging sessions are first clustered on the basis of similar user characteristics and their respective probability density functions are defined. From this, every charging session is predicted with a triplet of parameters, namely the arrival time, the charging duration and the average power expected during the process. The proposed method has been evaluated by considering a real case study. The results showed the ability to greatly improve the accuracy with respect to the chosen benchmark, both in terms of energy required by the station and the predicted number of charging sessions. The overall performance measured by Skill Score is 0.37 for the year 2019

Serena Guarracino, La traduzione messa in scena. Due rappresentazioni di Caryl Churchill in Italia (Perugia, Morlacchi Editore, 2017, 295 pp., ISBN 978-88-6074-896-6)

Recensione del volume "La traduzione messa in scena. Due rappresentazioni di Caryl Churchill in Italia" di Serena Guarracin

Beyond the Historical Record? Henry James in “The Master at St. Bartholomew’s Hospital 1914–1916”

The article analyses the short-story \u201cThe Master at St Bartholomew\u2019s Hospital 1914\u20131916\u201d by Joyce Carol Oates (2007) in the broader context of the Jamesian biofiction, a series of novels and tales featuring Henry James as their protagonist. The addition of the prefix \u201cbio-\u201d to \u201cfiction\u201d points out the hybrid nature of these texts, which are a melange of biography, autobiography, criticism and fiction. Oates\u2019s story not only epitomizes this hybridity, but it also proves to be an exploration of the potentiality of this subgenre to penetrate the mystery surrounding James\u2019s persona and saturate the lacunae in his biography by resorting to what David Lodge defined as \u201cthe novelist\u2019s licence\u201d. The short-story is yet another evidence of Oates\u2019s fascination with the unsaid in James\u2019s life and prose, because it revolves around the silence into which he sank at the outbreak of the Great War, when he did not write anything in his pocket diaries for three months. In an attempt to go beyond the limits of the historical record, Oates gives insights into the mind of the author by depicting a Henry James in crisis \u2013 nagged by doubts about his artistic legacy \u2013 in an atmosphere of uncertainty enhanced by a complex intertextual play. The result is \u2018a Henry James\u2019 slightly divergent from the historical one: thus, the tale advocates the inaccessibility of the private life of a real individual. Nonetheless, the acknowledgement of this limit spurs the celebration of fictional imagination

Elena Cotta Ramusino, Elizabeth Bowen’s Other Writing. A Study of Her Non-Fictional Prose, Como and Pavia, Ibis, 2018, 123 pp. ISBN 978-88-7164-579-7

Review of Cotta Ramusino's "Elizabeth Bowen\u2019s Other Writing. A Study of Her Non-Fictional Prose"

Hybrid Predictive Models for Accurate Forecasting in PV Systems

The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error

Characterization of Bifacial Photovoltaic Modules Based on I-V Curves Outdoor Measurement

Photovoltaic (PV) systems are well known for their simplicity of design, environmental friendliness, and low maintenance. Among the PV technologies, the behaviour of bifacial PV modules was studied in this research. Measurements of the I-V curves were carried out in the SolarTechLAB test facility at the Department of Energy of Politecnico di Milano, Italy, to detect the bifacial PV module behaviour, mainly in terms of power performance. In particular, I-V and power-voltage curves were measured at different tilt angles to consider several irradiance and cell temperature levels with both sides uncovered as well as with the back side covered. This last configuration was tested to evaluate the contribution of the rear face in the overall photoelectric conversion process. The comparison between the bifacial and monofacial operations highlighted that the power at the maximum power point of the bifacial operation can increase up to 13%. At the same time, leaving the rear face free allows for reducing the bifacial cell temperature up to about 6°C

Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview

In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS revolves around accurately determining the battery pack’s SOC. Notably, the advent of advanced microcontrollers and the availability of extensive datasets have contributed to the growing popularity and practicality of data-driven methodologies. This study examines the developments in SOC estimation over the past half-decade, explicitly focusing on data-driven estimation techniques. It comprehensively assesses the performance of each algorithm, considering the type of battery and various operational conditions. Additionally, intricate details concerning the models’ hyperparameters, including the number of layers, type of optimiser, and neuron, are provided for thorough examination. Most of the models analysed in the paper demonstrate strong performance, with both the MAE and RMSE for the estimation of SOC hovering around 2% or even lower

Transfer Learning Techniques for the Lithium-Ion Battery State of Charge Estimation

State of Charge (SOC) estimation is vital for battery management systems (BMS), impacting battery efficiency and lifespan. Accurate SOC estimation is challenging due to battery complexity and limited data for training Machine Learning based models. Transfer learning (TL) leverages pre-trained models, reducing training time and improving generalization in SOC estimation. In this paper, 8 different transfer learning techniques are examined, which were applied in four different models (LSTM, GRU, BiLSTM, and BiGRU) for SOC estimation. These transfer learning techniques have been applied to three datasets for re-training the models and results have been compared with the same models defined by Bayesian Hyperparameter Optimization. The TL4 and TL5 techniques consistently stood out as among the most efficient in both accuracy and computational time

Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power

In this paper an artificial neural network for photovoltaic plant energy forecasting is proposed and analyzed in terms of its sensitivity with respect to the input data sets. Furthermore, the accuracy of the method has been studied as a function of the training data sets and error definitions. The analysis is based on experimental activities carried out on a real photovoltaic power plant accompanied by clear sky model. In particular, this paper deals with the hourly energy prediction for all the daylight hours of the following day, based on 48 hours ahead weather forecast. This is very important due to the predictive features requested by smart grid application: renewable energy sources planning, in particular storage system sizing, and market of energy