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. Raw data, sampled at 1 sec and 1 minute are available from the different sensing devices, as well as synchronous data, with a common sampling interval of 5 minutes are available. Gaps existing within the data, as well as periods where interpolation was used, are available for each month of data.

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. Practically, prediction approaches can help anticipate events by ensuring good operation of the power network and maintaining a precise balance between the demand and supply of the power at every moment. In the literature, various estimation and forecasting methods have been developed. Artificial Neural Network (ANN) models are the most commonly used methods in solar irradiation prediction. This paper aims to firstly review, analyze, and provide an overview of different aspects required to develop an ANN model for solar irradiation prediction, such as data types, data horizon, data preprocessing, forecasting horizon, feature selection, and model type. Secondly, a highly detailed state of the art of ANN-based approaches including deep learning and hybrid ANN models for solar irradiation estimation and forecasting is presented. Finally, the factors influencing prediction model performances are discussed in order to propose recommendations, trends, and outlooks for future research in this field.

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., data not used for model design. An example using more than 11 years of water level data (593,178 samples) of the Carrión river collected at Villoldo gauge station shows that the MOGA framework can obtain low-complex models with excellent performance on unseen data, achieving an RMSE of 2.5 × 10-3, which compares favorably with results obtained by alternative design.

[How to Resume Elective Orthopaedic Surgical Procedures during the COVID-19 Pandemic?] / Como Retomar a Atividade Cirúrgica Eletiva em Ortopedia durante a Pandemia COVID-19?

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. It also describes safety rules for elective surgery and a model for monitoring patients after discharge according to scientific evidence.

Este documento foi elaborado pelo Colégio de Ortopedia da Ordem dos Médicos com o objetivo de estabelecer as orientações sobre a retoma da atividade cirúrgica programada em Ortopedia durante a pandemia COVID-19. As presentes normas de orientação: a) definem os critérios que permitem a priorização das cirurgias de acordo com a gravidade da situação clínica, com base em classificações existentes e publicadas; b) fornecem um modelo de organização para a preparação dos doentes, descrevendo os circuitos do doente nos períodos pré-operatório, intraoperatório e pós-operatório; c) realçam as regras de segurança para a realização de cirurgias e desenham um modelo de acompanhamento após a alta de acordo com a evidência científica.

Scientific Production on the Social Economy: A Review of Worldwide Research.

The aim of this article is to assess the use of the term Social Economy, while being aware of its lack of concreteness, and to analyze the level of scientific production by means of a bibliometric analysis using WoS (JCR) and Scopus (SJR) as sources. Starting in 2004 and related to the Charter of Principles of the Social Economy, the material development of articles began. The most receptive countries are Spain, the USA, China, the UK and Canada. In terms of the most productive journals, Voluntas in JCR and CIRIEC-Spain and REVESCO in SJR stand out. Scientific production on this issue is linked to university institutions, namely the Chinese Academy of Sciences, the University of Valencia and the University of Quebec. The most prevalent subject are Economics and Business in the case of JCR and Social Sciences in SJR. The most recognized term is that of cooperatives and the most prevalent keyword trends being related to sustainable development, climate change, urbanization, management and China.

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. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature.

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. METHODS: The employed models were radial basis functions neural networks with external dynamics induced by their inputs. Both the most suited set of model inputs and number of neurons in the network were found using the multi-objective genetic algorithm. The neural models were validated in two situations: the operating ones, as used in the construction of the network; and in 11 unseen situations. The new data addressed two new spatial locations and a new intensity level, assessing the intensity and space prediction capacity of the proposed model. RESULTS: Good performance was obtained during the validation process both in terms of the spatial points considered and whenever the new intensity level was within the range of applied intensities. A maximum absolute error of 0.5 degrees C+/-10% (0.5 degrees C is the gold-standard threshold in hyperthermia/diathermia) was attained with low computationally complex models. CONCLUSION: The results confirm that the proposed neuro-genetic approach enables foreseeing temperature propagation, in connection to intensity and space parameters, thus enabling the assessment of different operating situations with proper temperature resolution.

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. The neural models were assigned to estimate the temperature at different intensities and points arranged across the therapeutic transducer radial line (60 mm apart from the transducer face). Model inputs, as well as the number of neurons were selected using the multiobjective genetic algorithm (MOGA). The best attained models present, in average, a maximum absolute error less than 0.5 degrees C, which is pointed as the borderline between a reliable and an unreliable estimator in hyperthermia/diathermia. In order to test the spatial generalization capacity, the best models were tested using spatial points not yet assessed, and some of them presented a maximum absolute error inferior to 0.5 degrees C, being "elected" as the best models. It should be also stressed that these best models present implementational low-complexity, as desired for real-time applications.

Linear versus non-linear noninvasive temperature prediction in a homogeneous medium subjected to physiotherapeutic ultrasound / Comparação entre predição linear e não linear não-invasiva de temperatura num meio homogêneo sujeito a ultra-som de terapia

The lack of accurate time-spatial temperature estimators/predictors conditions the safe application of thermal therapies, such as hyperthermia. In this paper, a comparison between a linear and a non-linear class of models for non-invasive temperature prediction in a homogeneous medium, subjected to ultrasound at physiotherapeutic levels is presented. The linear models used were autoregressive with exogenous inputs (ARX) and the non-linear models were radial basis functions neural networks (RBFNN). In order to create and validate the models, an experiment was build to extract in vitro ultrasound RF-lines, as well as its correspondent temperature values. Then, features were extracted from the measured RF-lines and the models were trained and validated. For both the models, the best-fitted structures were selected using the multi-objective genetic algorithm (MOGA), given the enormous number of possible structures. The best RBFNN model presented a maximum absolute predictive error in the validation set five times less than the value presented by the best ARX model. In this work, the best RBFNN reached a maximum absolute error of 0.42 ºC, which is bellow the value pointed as a borderline between an appropriate and an undesired temperature estimator, which is 0.5 ºC. The average error was one order of magnitude less in the RBFNN case, and a less biased estimation was met. In addition, the best RBFNN needed less environmental information(inputs), given the capacity to non-linearly relate the information. The results obtained are encouraging, considering that coherent results should be obtained in a time-spatial modelling schema using RBFNN models.

A falta de estimadores de temperatura espaço-temporais que sejam precisos impede a aplicação segura das terapias térmicas, como por exemplo a hipertermia. Neste artigo é apresentada uma comparação entre uma classe de modelos lineares e uma classe de modelos não lineares, na predição não invasiva de temperatura num meio homogêneo, quando o mesmo é aquecido por ultra-som em níveis usados em fisioterapia. Os modelos lineares considerados foram do tipo auto-regressivo com entradas exógenas (ARX); a nível não-linear foram considerados redes neuronais RBF (RBFNN). Para treinar e validar os modelos foram recolhidas os ecos provenientes do meio, bem como os correspondentes valores de temperatura. Após a colheita de informação, foram extraídas características dos ecos medidos e posteriormente os modelos foram treinados e validados. Para ambas as classes de modelos, as melhores estruturas foram seleccionadas usando um algoritmo genético multi-objectivo (MOGA), devido ao número elevado de estruturas possíveis. O melhor modelo RBFNN apresentou um erro máximo absoluto cinco vezes inferior ao erro máximo absoluto apresentado pelo melhor modelo ARX. Neste trabalho, o melhor modelo RBFNN apresentou um erro máximo absolutode 0,42 ºC, valor este que é inferior ao limite (0,5 ºC) apresentado como sendo a fronteira entre um estimador desejado e um estimador indesejado. O erro médio cometido pelo melhor modelo neuronal é uma ordem de grandeza inferior ao erro médio apresentado pelo melhor modelo linear, obtendo-se deste modo uma estimação menos enviesada no caso das redes neuronais, com menos informação do ambiente (menos entradas) devido ao processamento não-linear dos dados de entrada. Os resultados obtidos são encorajadores, apontando no sentido de se obter bons resultados numa estimação espaço-temporal.