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
Forecasting heating and cooling energy demand in buildings plays a critical role in supporting building management and operation. Thus, analysing the energy consumption pattern of a building could help in the design of potential energy savings and also in operation fault detection, while contributing to provide proper indoor environmental conditions to the building's occupants.This paper aims at presenting the main results of a study consisting in forecasting the hourly heating and cooling demand of an office building located in Lisbon, Portugal, using machine learning models and analysing the influence of exogenous variables on those predictions. In order to forecast the heating and cooling demand of the considered building, some traditional models, such as linear and polynomial regression, were considered, as well as artificial neural networks and support vector regression, oriented to machine learning. The input parameters considered in the development of those models were the hourly heating and cooling energy historical records, the occupancy, solar gains through glazing and the outside dry-bulb temperature.The models developed were validated using the mean absolute error (MAE) and the root mean squared error (RMSE), used to compare the values obtained from machine learning models with data obtained through a building energy simulation performed on an adequately calibrated model.The proposed exploratory analysis is integrated in a research project focused on applying machine learning methodologies to support energy forecasting in buildings. Hence, the research line proposed in this article corresponds to a preliminary project task associated with feature selection/extraction and evaluation of potential use of machine learning methods. © 2020 IEEE.

Abstract
Lubricant oil is an essential element in wind and hydropower generation. We present a lifecycle assessment (LCA) of the lubricant oils (mineral, synthetic and biodegradable) used in hydropower and wind power generation. The results are given in terms of energy used, associated emissions and costs. We find that, for the oil turbines and regulation systems considered here, biodegradable oil is a better option in terms of energy and CO2 equivalent emissions than mineral or synthetic oils, from production and recycling through to handling. However, synthetic and mineral oils are better options due to the potential risks associated with the use of biodegradable oil, generally when it comes into contact with water. There are also significant savings to be made in the operation of wind turbines when using an improved type of synthetic oil.

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Energy hub system (EHS) incorporating multiple energy carriers, storage, and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation. This article proposes an innovative distributionally robust optimization model to operate EHS with an energy storage system (ESS), considering the multimodal forecast errors of photovoltaic (PV) power. Both battery and heat storage are utilized to smooth PV output fluctuation and improve the energy efficiency of EHS. This article proposes a novel multimodal ambiguity set to capture the stochastic characteristics of PV multimodality. A two-stage scheme is adopted, where 1) the first stage optimizes EHS operation cost, and 2) the second stage implements real-time dispatch after the realization of PV output uncertainty. The aim is to overcome the conservatism of multimodal distribution uncertainties modeled by typical ambiguity sets and reduce the operation cost of EHS. The presented model is reformulated as a tractable semidefinite programming problem and solved by a constraint generation algorithm. Its performance is extensively compared with widely used normal and unimodal ambiguity sets. The results from this article justify the effectiveness and performance of the proposed method compared to conventional models, which can help EHS operators to economically consume energy and use ESS wisely through the optimal coordination of multienergy carriers.