Abstract
At a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.

Abstract
The experimental investigation of building-integrated photovoltaic thermal (BIPVT) solar systems is essential to characterise the operation of these elements under real conditions of use according to the climate and building type they pertain. BIPVT systems can increase and ensure energy performance and readiness without jeopardising the occupant comfort if correctly operated. The present work presents a case study's experimental analysis composed of a BIPVT system for heat recovery located in a controlled test room. This work contribution focuses on the presentation of the obtained measured value results that correspond to the BIPVT main boundary conditions (weather and room characteristics) and the thermal behaviour and performance of the BIPVT system, located in the Solar XXI Building, a nZEB exposed to the mild Mediterranean climate conditions of Portugal.

Abstract
Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R-2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.

Abstract
Home Energy Management Systems (HEMS) are becoming progressively more researched and employed to invert the continuously increasing trend in (electrical) energy consumption in buildings. One of the critical aspects of any HEMS is the real-time monitoring of all variables related to the management system, as well as the real-time control of schedulable electric appliances. This paper describes a data acquisition system implemented in a residential house in the South of Portugal. With the small amount of data collected, a Radial Basis Function (RBF) model, designed by a Multi-objective Genetic Algorithm (MOGA) framework, to forecast total electric consumption was developed. Results show that, even with these little data, the model can be used in a predictive control scheduling mechanism for HEMS. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
O uso de meios de transporte elétricos está em plena expansão. Esse fato nos leva a implementação de novas tecnologias relacionadas ao seu método de carregamento. Atualmente, o carregamento sem fio, conhecido como sistema WPT (Wireless Power Transfer), está sendo amplamente estudado e implementado. Assim, este trabalho apresenta uma revisão bibliográfica dos principais aspectos e características desse sistema, além de seus estágios de operação. Com base nessas informações, é proposto o projeto de um carregador WPT de 5 kW operando em 85 kHz, respeitando os padrões previstos na norma SAE J2954. Por fim, o circuito é simulado para validação da metodologia de projeto.

Abstract
Nowadays, as more data is now available from an increasing number of installed sensors, load forecasting applied to buildings is being increasingly explored. The amount and quality of resulting information can provide inputs for smarter decisions when managing and operating office buildings. In this article, the authors use two data-driven methods (artificial neural networks and support vector machines) to predict the heating and cooling energy demand in an office building located in Lisbon, Portugal. In the present case-study, these methods prove to be an accurate and appealing alternative to the use of accurate but time-consuming multi-zone dynamic simulation tools, which strongly depend on several parameters to be inserted and user expertise to calibrate the model. Artificial neural networks and support vector machines were developed and parametrized using historical data and different sets of exogenous variables to encounter the best performance combinations for both the heating and cooling periods of a year. In the case of support vector regression, a variation introduced simulated annealing to guide the search for different combinations of hyperparameters. After a feature selection stage for each individual method, the results for the different methods were compared, based on error metrics and distributions. The outputs of the study include the most suitable methodology for each season, and also the features (historical load records, but also exogenous features such as outdoor temperature, relative humidity or occupancy profile) that led to the most accurate models. Results clearly show there is a potential for faster, yet accurate machine-learning based forecasting methods to replace well-established, very accurate but time-consuming multi-zone dynamic simulation tools to forecast building energy consumption.