From home energy management systems to energy communities: methods and data.

This paper introduces the HEMStoEC database, which contains data recorded in the course of two research projects, NILMforIHEM , and HEMS2IEA , for more than three years. To be manageable, the dataset is divided in months, from January 2020 until February 2023. It consists in: (a) consumption electric data for four houses in a neighbourhood situated in the south of Portugal, (b) weather data for that location, (c) photovoltaic and battery data, (d) inside climate data, and (e) operation of several electric devices in one of the four houses.

A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trends.

Solar irradiation data are imperatively required for any solar energy-based project. The non-accessibility and uncertainty of these data can greatly affect the implementation, management, and performance of photovoltaic or thermal systems. Developing solar irradiation estimation and forecasting approaches is an effective way to overcome these issues.

Optimized Design of Neural Networks for a River Water Level Prediction System.

In this paper, a Multi-Objective Genetic Algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that, given the data at hand, can partition it into datasets and is able to determine a near-optimal model with the right topology and inputs, offering a good performance on unseen data, i.e.

[How to Resume Elective Orthopaedic Surgical Procedures during the COVID-19 Pandemic?].

This document was prepared by the College of Orthopedics of the Portuguese Medical Association with the aim of developing the guidelines on the resumption of elective surgical activity in Orthopedics during the COVID-19 pandemic. It sets the criteria that allow the prioritization of surgeries according to the severity of the clinical situation, based on existing and published classifications. Moreover, it provides an organizational model for patient preparation and describes the patient pathways in the preoperative, intraoperative and postoperative periods.

An Intelligent Weather Station.

Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.

A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature.

Accurate measurements of global solar radiation and atmospheric temperature,as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm.

Neuro-genetic non-invasive temperature estimation: intensity and spatial prediction.

Objectives: The existence of proper non-invasive temperature estimators is an essential aspect when thermal therapy applications are envisaged. These estimators must be good predictors to enable temperature estimation at different operational situations, providing better control of the therapeutic instrumentation. In this work, radial basis functions artificial neural networks were constructed to access temperature evolution on an ultrasound insonated medium.

A soft-computing methodology for noninvasive time-spatial temperature estimation.

The safe and effective application of thermal therapies is restricted due to lack of reliable noninvasive temperature estimators. In this paper, the temporal echo-shifts of backscattered ultrasound signals, collected from a gel-based phantom, were tracked and assigned with the past temperature values as radial basis functions neural networks input information. The phantom was heated using a piston-like therapeutic ultrasound transducer.