Abstract
Industry 4.0 represents a turning point in the thinking of the production model since it is based on digitalized production systems with the aim of improving productivity, product quality, and delivery time to the customer. The digitalization and evolution of information technology allowed the emulation of production system virtual models, namely in the concept of Digital Twin (DT), with the ability to simulate different scenarios providing support for better decision making. This concept not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of the production activity according to the fulfillment of the intended business goals. In literature, the concept of the Digital Twin is exhaustively treated as a stand-alone factory (one digital factory represents one physical factory) and underestimates the possibility of a DT oriented to a customized product (a project) that requires decentralized production systems. This paper brings to discussion the relevance of product customized applying DT to smart customization, and the inclusion of decentralized production systems supported by Cloud Manufacturing.

Abstract
This paper introduces a detailed procedure for executing a black start service from an offshore wind farm (OWF) through the integration of grid-forming (GFM) control. The proposed strategy involves exploiting a grid-forming battery energy storage system (BESS) to deliver black start service within an OWF equipped with grid-following wind turbines. Controller modelling, and operation methodology are explained. To illustrate the efficacy of the suggested control and operation principles, the study employs an OWF as a case study. Simulation analyses are conducted using the Matlab/Simulink software to demonstrate the viability of the presented strategy.

Abstract
As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as shoeboxes using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning.

Abstract

Abstract
Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) in adults can present challenges in learning and work environments, by impacting focus, organization, social interaction, and self-esteem. The aim of this study is the potential of Human-Computer Interaction (HCI) in the empowerment of adults with ADHD and ASD. Specific difficulties faced in educational and professional settings were found through qualitative interviews with six participants. HCI seems to offer a pathway towards a more inclusive future, as educational technology solutions built on HCI principles can create better and alternative learning environments with fewer distractions and gamification for increased engagement. Assistive technologies can address challenges related to focus and organization (like task management apps, time tracking tools). Additionally, features promoting social interaction and communication can empower individuals with ASD. Technologies arising nowadays like Augmented and Virtual Reality (AR/VR) can create interactive learning experiences. Through the use of HumanComputer Interaction principles, more inclusive learning and work environments that empower individuals with ADHD and ASD can originate, while improving engagement and efficiency for all.

Abstract
This work aimed to develop methodologies for analysing statistical correlations among wind data series using various Measure-Correlate-Predict (MCP) methods, with the goal of selecting the most suitable method for extrapolating long-term data with minimal associated uncertainty. It was analysed the minimum time required for a wind measurement campaign when applying this methodology. Fifteen local wind measurement stations were selected. The long-term wind data reanalysis series that exhibited the strongest correlation with the measured wind data at each station was then chosen. Multiple tests were conducted with different simultaneous periods between the measured data series and the long-term series. Fifteen correlation algorithms were tested for each concurrent period. The performance of each model was evaluated using the RMSE (Root Mean Square Error) and MBE (Mean Bias Error) associated with each MCP. Analysis of the errors identified measurement periods with the lowest associated error ranging from 1 to 5 years and a single-factor ANOVA analysis was conducted. Finally, t-significance tests were performed. The study concluded that the Neural Network was the most effective MCP method. Additionally, it was determined that the minimum number of years required for a local measurement campaign should be between 2 and 3 years. © 2024, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.