Abstract
Sustainable agricultural production requires a controlled usage of water, nutrients, and minerals from the environment. Different strategies of plant irrigation are being studied to control the quantity and quality balance of the fruits. Regarding efficient irrigation, particularly in deficit irrigation strategies, it is essential to act according to water stress status in the plant. For example, in the vine, to improve the quality of the grapes, the plants are deprived of water until they reach particular water stress before re-watered in specified phenological stages. The water status inside the plant is estimated by measuring either the Leaf Potential during the Predawn or soil water potential, along with the root zones. Measuring soil water potential has the advantage of being independent of diurnal atmospheric variations. However, this method has many logistic problems, making it very hard to apply along all the yard, especially the big ones. In this study, the Predawn Leaf Water Potential (PLWP) is daily predicted by Machine Learning models using data such as grapes variety, soil characteristics, irrigation schedules, and meteorological data. The benefits of these techniques are the reduction of the manual work of measuring PLWP and the capacity to implement those models on a larger scale by predicting PLWP up to 7 days which should enhance the ability to optimize the irrigation plan while the quantity and quality of the crop are under control.

Abstract
The combination of consumer owned Distributed Energy Resources, new Information and Communication Technologies (ICT), as well as changes to the national electricity regulations have created new opportunities for consumer engagement in the electricity sector. In this paper, this combination of technologies and regulations is examined in the Portuguese context. The new regulations dealing with self-consumption from prosumers are combined with smart contracts and distributed ledger technology to formulate an automated energy trading system for residential end-users in local energy markets. Results show that including prosumers in the local energy market brings significant benefits to all market participants. Additionally, results show that the newly created regulatory role of a Market Facilitator is beneficial to these type of local energy exchanges.

Abstract
A novel algorithm to separate S2 heart sounds into their aortic and pulmonary components is proposed. This approach is based on the assumption that, in different heartbeats of a given recording, aortic and pulmonary components maintain the same waveform but with different relative delays, which are induced by the variation of the thoracic pressure at different respiration phases. The proposed algorithm then retrieves the aortic and pulmonary components as the solution of an optimization problem which is approximated via alternating optimization. The proposed approach is shown to provide reconstructions of aortic and pulmonary components with normalized root mean-squared error consistently below 10% in various operational regimes.

Abstract
High penetration of distributed energy resources in distribution networks is facilitated through the microgrids (MGs) structure. From the technical point of view, the MG operator (MGO) is responsible for the internal operation of the MG regarding which the distribution system operator (DSO) cannot take any decision. From the market viewpoint, the MGO participates in the wholesale markets regarding which the scheduling of the MG's resources is monitored. Therefore, the operation problem of the MGO considering its participation in the wholesale markets under uncertainty has been investigated in many studies. In this paper, a two-stage stochastic optimization approach is developed to model the MGO's bidding strategies in the day-ahead energy and reserve markets considering its stochastic decisions in a real-time market. In this model, the uncertainties of demand, wind speed, and solar radiation are modeled through different scenarios using the probability distribution functions (PDFs) of these parameters. Moreover, the uncertainty of the real-time energy price is modeled using the information gap decision theory (IGDT) method. To show the effectiveness of the model, it is applied on a MG test system.

Abstract
Smart4RES is a European Horizon2020 project developing next generation solutions for renewable energy forecasting. This paper presents highlight results obtained during the first year of the project. Data science is used throughout the proposed solutions in order to process the large amount of heterogeneous data available to forecasters, and derive model-free approaches of forecasting and decision-aid tasks. This paper presents a series of solutions addressing relevant for Photovoltaics (PV) and storage applications. High-resolution Numerical Weather Predictions and regional solar irradiance forecasting provide detailed information on local weather conditions and their variability. PV power forecasting benefits from such new data sources, but also the proposed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management. © 2021 Energynautics GMBH.

Abstract
The economic sector of tourism has gained significant weight in the economy of many countries, highlighting the weight of this sector in Portugal. However, the inconsistency and seasonality of demand causes companies linked to the sector to encounter difficulties regarding the planning and management of resources allocated to the activity. It is often the case that there are periods of economic loss caused by a small volume of demand that is insufficient to support the costs of the activity. In this context, this article proposes a system that, based on intelligent data analysis, allows a hotel chain to segment customers and enhance exclusive offers to minimize fluctuation and demand gaps in hotel units installed in thermal instances.