Abstract
Conducting epidemiological and risk assessment research that considers the exposome concept, as in the case of HEALS project, requires the acquisition of higher dimension data sets of an increased complexity. In this context, new methods that provide accurate and interpretable data summary on relevant environmental factors are of major importance. In this work, a questionnaire was developed to collect harmonized data on potential pollutant sources to air in the indoor environment where children spend an important part of their early life. The questionnaire was designed in a user friendly checklist format to be filled out at the maternity in ten European cities. This paper presents and discusses the rationale for the selection of the questionnaire contents and the results obtained from its application in the households of 309 HEALS-enrolled families with babies recently born in Porto, Portugal. The tool was very effective in providing data on the putative air pollution sources in homes, with special focus on the bedroom of the newborns. The data collected is part of a wider effort to build the databases and risk assessment models of the HEALS project. The results of the analysis of the collected data suggest that, for the population under study, the main concerns on early life exposures at home can be related to emissions from the use of household solid fuels, indoor tobacco, household cleaning products, fragranced consumer products (e.g. air fresheners, incense and candles), moisture-related pathologies and traffic-related outdoor pollution. Furthermore, it is anticipated that the tool can be a valuable means to empower citizens to actively participate in the control of their own exposures at home. Within this context, the application of the checklist will also allow local stakeholders to identify buildings presenting most evident IAQ problems for sampling or intervention as well as to guide them in preparing evidence-based educational/awareness campaigns to promote public health through creating healthy households.

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
In this paper, Lagrangian dual formulation is used to solve the Environmental/Economic Dispatch problem. The proposed method, that results quite different from the metaheuristic methods employed in literature, was tested on a six generating units system. The results obtained improve others reported in previous investigations, by simultaneously diminishing the total fuel cost and pollutants emissions.

Abstract
In this paper a duality theory approach is proposed for solving the environmental/economic dispatch problem. For the multiobjective problem scalarization, weighted sum method is used and the associated dual problem is solved using a quadratic programming algorithm. This strategy is tested on three systems with different number of generators and characteristics. The obtained results are compared with other previously reported, showing some advantages of the proposed approach.

Abstract