Abstract
The European Union aims to achieve a nearly zero energy balance in buildings by 2020. The present study takes into consideration the passive systems of the building, energy demand, and energy generated by the on-site photovoltaic and storage system, and how they interact in different scenarios. The study also considers the energy demand from the grid and the surplus of renewable energy. The software EnergyPlus was used and the parametric sensitivity simulation method was applied, taking into account blinds operation, ventilation strategies, HVAC operation schemes and battery storage capacity, in 96 scenarios. The results highlight that there is great variability between the considered scenarios, highlighting the importance of sizing methodologies for the passive systems and the use of optimized home management algorithms. It was found that the use of batteries with higher storage capacity increases the demand-supply from the on-site PV energy but decreases the amount of energy injected into the grid. The design of the PV and battery system based on yearly integrated simulations allows for an optimized solution. This study also emphasizes the importance of knowing the expected occupancy during the design phase, as a significant input to the sizing methodologies of the storage capacity and on-site generation.

Abstract
Prediction of the energy consumption is a key aspect of home energy management systems, whose aim is to increase the occupant’s comfort while reducing the energy consumption. This work, employing three years measured data, uses radial basis function neural networks, designed using a multi-objective genetic algorithm (MOGA) framework, for the prediction of total electric power consumption, HVAC demand and other loads demand. The prediction horizon desired is 12 h, using 15 min step ahead model, in a multi-step ahead fashion. To reduce the uncertainty, making use of the preferred set MOGA output, a model ensemble technique is proposed which achieves excellent forecast results, comparing additionally very favorably with existing approaches. © 2020, Springer Nature Switzerland AG.

Abstract
This study addresses the thermal and energy performance assessment of a Building Integrated Photovoltaic Thermal (BIPVT) system installed on the facade of a test room in Solar XXI, a Net Zero Energy Building (NZEB) located in Lisbon, Portugal. A numerical analysis using the dynamic simulation tool EnergyPlus was carried out for assessing the performance of the test room with the BIPVT integrated on its facade through a parametric analysis of 14 scenarios in two conditions: a) receiving direct solar gains on the glazing surface and b) avoiding direct solar gains on the glazing surface. Additionally, a computational fluid dynamics (CFD) analysis of the BIPVT system was performed using ANSYS Fluent. The findings of this work demonstrate that the BIPVT has a good potential to improve the sustainability of the building by reducing the nominal energy needs to achieve thermal comfort, reducing up to 48% the total energy needs for heating and cooling compared to the base case. The operation mode must be adjusted to the other strategies already implemented in the room (e.g., the presence of windows and blinds to control direct solar gains), and the automatic operation mode has proven to have a better performance in the scope of this work.

Abstract

Abstract
The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.

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.