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

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

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

Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic

Electric Vehicles Charging Sessions Classification Technique for Optimized Battery Charge Based on Machine Learning

The fast increase in electric vehicle (EV) usage in the last 10 years has raised the need to properly forecast their energy consumption during charge. Lithium-ion batteries have become the major storage component for electric vehicles, avoiding their overcharge can preserve their health and prolong their lifetime. This paper proposes a Machine Learning model based on the K-Nearest Neighbors classification algorithm for EV charging session duration forecast. The model forecasts the duration of the charge by assigning the event to its correct class. Each class contains the charging events whose duration is comprised of a certain interval. The only information used by the algorithm is the one available at the beginning of the charging event (arrival time, starting SOC, calendar data). The model is validated on a real-world dataset containing records of charging sessions from more than 100 users, a sensitivity analysis is performed to assess the impact of different information given as input. The effectiveness of the model with respect to the benchmark models is demonstrated with an increase in performance

Laparoscopic paraesophageal hernia repair with absorbable mesh: a systematic review

Background: Laparoscopic repair is the standard of care for patients with paraesophageal hernia (PEH). Different prosthetic materials have been proposed to bolster the hiatus thus theoretically minimizing the probability for hernia recurrence. The use of non-absorbable mesh has been reported however, their safety profile has been questioned because the noteworthy mesh-related complication rate. Opposite, absorbable mesh (synthetic and biologic) seems associated with mitigated mesh-related complications and comparable hernia recurrence in the short- and medium-term. Methods: PubMed, MEDLINE, EMBASE, Scopus, Google Scholar, and ClinicalTrials.gov were executed according to the PRISMA statement until May 2022. Primary endpoints were technical details and surgical outcomes of adult patients (>= 18 years old) that underwent laparoscopic PEH repair and crural reinforcement with absorbable mesh. The ROBINS-I tool was used to assess the methodological quality of included studies. Results: Thirty-nine studies (3,103 patients) were included. The age of the patient population ranged from 18 to 93 years old and 62.8% were females. Posterior cruroplasty was performed in all patients. U-shape (83.7%), circumferential (8.1%), keyhole (5.4%) and starburst (2.8%) mesh configuration were described. Different methods for mesh fixation (sutures vs. fibrin glue vs. absorbable tacks) were adopted while Nissen (75.1%) and Toupet (21.1%) fundoplication were mainly fashioned. The overall postoperative complication rate was 2.5%. Pulmonary and cardiac complication rates were 1.8% and 0.9%, respectively while in-hospital mortality was 0.2%. Postoperative follow-up ranged from 12 to 166 months. Mesh-related complication rate was 0.06% (esophageal stricture related to fibrosis). Hernia recurrence rate was 12.7% while re-do surgery was required in 1.9% of patients. Postoperative dysphagia rate was 5.1%. Discussion: Consensus concerning the optimal mesh material for crural buttressing is lacking. Given the potential for tissue ingrowth rather than encapsulation and reduced degree of perivisceral inflammation, absorbable meshes are mostly preferred over non-absorbable meshes. The use of absorbable mesh seems safe and effective with low overall and mesh-related complications, acceptable recurrence rate and low need for re-do surgery in the short/medium-term. Because heterogeneity related to different hernia characteristics, intraoperative technical variations (i.e., method for mesh fixation, etc.), definition of hernia recurrence and diverse follow-up, a conclusive evidence is still to be defined

Redes de intercâmbio de sementes como estratégia de conservação da agrobiodiversidade: estudo de caso do milho pipoca no Sul do Brasil

A região Extremo Oeste de Santa Catarina, Sul do Brasil, tem sido caracterizada por apresentar uma importante diversidade de variedades locais de milho pipoca, conservada in situ-on farm principalmente pelas mulheres.O objetivo do estudo foi identificar e analisar as redes troca de sementes de milho pipoca nos municípios de Anchieta e Guaraciaba. Foram entrevistadas 244 agricultoras, no período de janeiro a julho de 2013. Foi estimada a densidade da rede, a qual indica seu nível de fragmentação.Entre 2011 e 2013 foram intercambiadas 92 variedades locais.O valor da densidade da rede foi de 0,4. Verificou-se queos processos de trocas de sementes permanecem ativos, caracterizados, sobretudo, pelas relações de vizinhança.As redes de intercâmbio de sementes são importantes mecanismos de conservação da diversidade de milho pipoca da região.The region Far West of Santa Catarina, southern Brazil, has been characterized by presenting an important diversity of local varieties of popcorn, preserved in situ on-farm primarily by women. The aim of the study was to identify and analyze the networks seeds exchange popcorn in the municipalities of Anchieta and Guaraciaba. 244 farmers were interviewed in the period from January to July 2013. It was estimated the density of the network, which indicates their level of fragmentation. Between 2011 and 2013 were exchanged 92 landraces. The amount of network density was 0.4. It has been found that the seeds remain active exchange processes, characterized mainly by neighborhood relations. Networks seeds exchange are important conservation mechanisms popcorn diversity of the region.Eje A6: Desarrollo Rural, Movimientos Sociales, Estado y AgroecologíaFacultad de Ciencias Agrarias y Forestale

Redes de intercâmbio de sementes como estratégia de conservação da agrobiodiversidade: estudo de caso do milho pipoca no Sul do Brasil

A região Extremo Oeste de Santa Catarina, Sul do Brasil, tem sido caracterizada por apresentar uma importante diversidade de variedades locais de milho pipoca, conservada in situ-on farm principalmente pelas mulheres.O objetivo do estudo foi identificar e analisar as redes troca de sementes de milho pipoca nos municípios de Anchieta e Guaraciaba. Foram entrevistadas 244 agricultoras, no período de janeiro a julho de 2013. Foi estimada a densidade da rede, a qual indica seu nível de fragmentação.Entre 2011 e 2013 foram intercambiadas 92 variedades locais.O valor da densidade da rede foi de 0,4. Verificou-se queos processos de trocas de sementes permanecem ativos, caracterizados, sobretudo, pelas relações de vizinhança.As redes de intercâmbio de sementes são importantes mecanismos de conservação da diversidade de milho pipoca da região.The region Far West of Santa Catarina, southern Brazil, has been characterized by presenting an important diversity of local varieties of popcorn, preserved in situ on-farm primarily by women. The aim of the study was to identify and analyze the networks seeds exchange popcorn in the municipalities of Anchieta and Guaraciaba. 244 farmers were interviewed in the period from January to July 2013. It was estimated the density of the network, which indicates their level of fragmentation. Between 2011 and 2013 were exchanged 92 landraces. The amount of network density was 0.4. It has been found that the seeds remain active exchange processes, characterized mainly by neighborhood relations. Networks seeds exchange are important conservation mechanisms popcorn diversity of the region.Eje A6: Desarrollo Rural, Movimientos Sociales, Estado y AgroecologíaFacultad de Ciencias Agrarias y Forestale

Pitch as the Main Determiner of Italian Lexical Stress Perception Across the Lifespan : Evidence From Typical Development and Dyslexia

The study deals with the issue of lexical stress perception in both a developmental (comparing children and adults with typical development) and a clinical perspective (comparing typically developing children and children with dyslexia). The three parameters characterizing the acoustic profiles of words and non-words in a certain language are duration, pitch and intensity of its syllables. Based on (sparse) previous literature on Italian and other European languages, it was expected that syllable duration would be the parameter predominantly determining the perception of stress position. It was furthermore anticipated that children with dyslexia may be found to have an altered perception of lexical stress, due to their impairments in auditory processing of either pitch, duration or (more controversial) intensity. Systematic manipulation of the pitch, duration and intensity profiles of three Italian trisyllabic non-words produced a series of 81 stimuli, that were judged with respect to stress position (perceived on the ultimate, penultimate, or antepenultimate syllable) by the three groups of participants. The results showed, contrarily to expectations, that the pitch component is the most reliable acoustic cue in stress perception for both adults, in whom this dominance is very strong, and typically developing children, who showed a similar but quantitatively less marked pattern. As to children with dyslexia, they did not seem to rely on any parameter for their judgments, and rather gave random responses, which point to a general inability to process the various acoustic modulations that normally contribute to stress perception. Performance on the stress perception task strongly correlates with language (morphosyntactic) measures in the whole sample of children, and with reading abilities in the group with dyslexia, confirming the strict relationship between the two sets of skills. These findings seem to support a language-specific approach, suggesting that the set of acoustic parameters required for the development of stress perception is language-dependent rather than universal