Abstract
The buildings' energy consumption increasing requires solutions to improve their energy efficiency, thus reducing the electricity bill's associated costs. This paper aims to study the load profiles of a service building and its optimization to reduce the costs related to electricity consumption. The electrical load profiles are analyzed, and the electrical equipment and its consumption are characterized. Moreover, to increase energy efficiency and reduce energy costs, a renewable energy system based on photovoltaic panels is sized and integrated into the building. The analysis of the building's consumption profiles allowed the PV system's dimensioning to eliminate power peaks, enabling a reduction in the contracted power. The results demonstrate the effectiveness of the proposed solution, resulting in a reduction of the electricity bill.

Abstract
The trend towards a decentralized, decarbonized, and digital energy system is gaining momentum. A key driver of this change is the rapid penetration increase of Distributed Energy Resources (DER). Commercial consumers can offer significant contributions to future energy systems, especially by engaging in demand response services. Virtual Power Plants (VPP) can aggregate and operate DERs to provide the required energy to the local grid and allowing for the participation in wholesale energy markets. This work considers both the technical constraints of the distribution system as well as the commercial consumer's comfort preferences. A stochastic mixed-integer linear programming (MILP) optimization model is developed to optimize the scheduling of various DERs owned by commercial consumers to maximize the profit of the TVPP. A case study on the IEEE 119-bus test system is carried out. Results from the case study show that the TVPP provides optimal DER scheduling, improved system reliability and increase in demand response engagement, while maintaining commercial consumer comfort levels. In addition, the profit of the TVPP increases by 49.23% relative to the baseline scenario.

Abstract
It was a recognized challenge of lack of ventilators needed to face COVID-19 worldwide. Although ventilators are sparse, self-inflating manual resuscitators are widely available in-hospital services, providing a rapid response to respiratory depression. Based on this, a device (PNEUMA) This work describes the use of a simulation environment to test the usability of a novel device to automate self-inflating manual resuscitators.The usability study was divided into two parts: (1) participants followed a protocol with instructions for assembling and using the system in a non-clinical context (Usability testing. Left panel – assembly of the system (part I); right panel – use of the system in an immersive clinical simulation environment (part II).A convenience sample (two MDs and six RNs) from an intensive care unit of a tertiary Portuguese hospital participated in the test. Usability testing showed that the system was easy and timely assembled, with low complexity of use (e.g. not requiring external help). The clinical scenario tested the transition between spontaneous and mechanical ventilation, and ventilatory parameters’ control, using PNEUMA. All participants reported that the controllable parameters (I:E, RR, Vol, PIP, Plat, and PEEP) were relevant and easy to change. Participants suggested the inclusion of patient parameters such as the tidal volume and lung compliance. Participants also suggested improvements, such as the inclusion of pressure alarms and a more user-friendly interface. All participants reported that they would be willing to use the device for emergency use.The reported study resulted in recommendations and ameliorations of the device, before its use in real settings, in the context of the COVID-19 pandemic. The use of simulation environments for device/systems’ testing provides a timely and standardized approach, enabling a safer clinical practice.

Abstract

Abstract
The FAO-56 Penman-Monteith (PM) equation is regarded as the most accurate equation to estimate reference evapotranspiration (ETo). However, it requires a broad range of data that may not be available or of reasonable quality. In this study, nine temperature-based methods were assessed for ETo estimation during the irrigation at fourteen locations distributed through a hot-summer Mediterranean climate region of Alentejo, Southern Portugal. Additionally, for each location, the Hargreaves-Samani radiation adjustment coefficient (k(Rs)) was calibrated and validated to evaluate the appropriateness of using the standard value, creating a locally adjusted Hargreaves-Samani (HS) equation. The accuracy of each method was evaluated by statistically comparing their results with those obtained by PM. Results show that the calibration of the k(Rs), a locally adjusted HS method can be used to estimate daily ETo acceptably well, with RMSE lower than 0.88 mm day(-1), an estimation error lower than 4% and a R-2 higher than 0.69, proving to be the most accurate model for 8 (out of 14) locations. A modified Hargreaves-Samani method also performed acceptably for 4 locations, with a RMSE of 0.72-0.84 mm day(-1), a slope varying from 0.95 to 1.01 and a R-2 higher than 0.78. One can conclude that, when weather data is missing, a calibrated HS equation is adequate to estimate ETo during the irrigation season.

Abstract