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

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

Left extralobar pulmonary sequestration and a right aorto-topulmonary vein fistula in a newborn : a 3-mm thoracoscopic monolateral approach

An extralobar pulmonary sequestration (EPS) associated with a contralateral aorto-to-pulmonary vein fistula is rare. We report the case of a female newborn with left EPS fed by an artery originating from the distal thoracic aorta and, symmetrically on the controlateral side, an artery shunting in the inferior right pulmonary vein. Echocardiography showed dilatation of the left atrium. On the 34th day since birth (weight 4500 g), the patient was operated on thoracoscopically. The EPS was closed with a 3-mm sealing system, divided and removed. A window in the mediastinal pleura was created, and the origin of the fistula was identified and sealed. The postoperative course was uneventful. The patient was discharged on Day 4 with no echocardiographic signs of persistence of the fistula and of the congestive heart failure. This is the first case report of a thoracic large systemic circulation-to-pulmonary vein fistula causing heart failure associated with EPS. The thoracoscopic monolateral approach and the availability of 3-mm instruments guaranteed a maximum level of minimal invasiveness

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

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

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

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

duodenal hematoma and pancreatitis complicating endoscopic intestinal biopsy in a boy with noonan syndrome

Duodenal intramural hematoma is a rare condition, mostly described in children and young adults that can be a complication of duodenal biopsy, especially in patients with predisposing hemorrhagic diathesis. It can determine secondary pancreatitis because of ampullary hematoma. Noonan Syndrome (NS) is an autosomal dominant disorder characterized by short stature, typical facial dysmorphisms, congenital heart defects and other anomalies such as bleeding problems which have been reported in up to 55% of patients. We herein report a case of duodenal hematoma with pancreatitis developed after endoscopic biopsy in a boy who was initially suspected of having celiac disease on the base of his short stature and growth retardation. Afterwards a more careful past medical history collection and objective examination revealed characteristic features of NS which could have been picked-up in advance, thus avoiding an investigation, such as the duodenal endoscopic biopsy, which in NS patient is potentially more risky