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
O sistema de distribuição de energia elétrica é a parcela do sistema de potência mais vulnerável aos eventos de interrupção, originados por fatores naturais externos ou intrínsecos aos equipamentos elétricos. Na mitigação dos impactos desses eventos, simuladores digitais em tempo real são utilizados na obtenção dos transitórios do sistema elétrico, todavia, essa técnica demanda grande esforço computacional. Nesse contexto, propõe-se uma técnica híbrida para simulação do transitório em sistemas de distribuição, combinando a alta taxa de amostragem dos modelos no domínio do tempo para monitoramento de tensão e corrente com a velocidade de processamento dos algoritmos que operam os modelos fasoriais em regime quase-estacionário, ou permanente. A metodologia proposta também permite considerar diferentes tecnologias de geradores distribuídos acoplados na rede. Os resultados dos testes realizados indicam a consistência da metodologia proposta, representando o comportamento do transitório no sistema de distribuição de energia elétrica. Todas as simulações realizadas são comparadas com valores amostrais obtidos usando um software comercial especializado.

Abstract
This paper models the role of electricity tariffs on the long-term adoption of photovoltaic and storage technologies as well as the consequent impact on the distribution grid. An adoption model that captures the economic rationality of tariff-driven investments and considers the stochastic nature of individual consumers' decisions is proposed. This model is then combined with a probabilistic load flow to evaluate the long-term impacts of the adoption on the voltage profiles of the distribution grid. To illustrate the methodology, different components of the electricity tariffs, including solar compensation mechanisms and time differentiation of Time-of-Use (ToU) rates, are evaluated, using a case study involving a section of a medium-voltage network with 118 nodes.

Abstract
The total losses volume represents a substantial amount of energy and, consequently, a large cost that is often included in the tariffs structure. Uneven connection of single-phase loads is a major cause for three-phase unbalance and a fundamental cause for active power losses, particularly in Low Voltage (LV) networks. This paper analyzes the impact of load unbalance on LV network losses. In the first phase, several load scenarios per phase are considered to characterize how losses depend on load unbalance. The second phase examines the data collected per phase on a set of real networks, aiming at illustrating real-world cases. The third phase analyzes the effect that public lighting and microgeneration may have in the load unbalance and on the subsequent energy losses. The results of this work clearly demonstrate that it is possible to reduce three-phase unbalance (and losses) through a judicious distribution of loads and microgeneration.

Abstract
Global climate change is currently a focus issue because of its impacts on the most diverse natural systems and, consequently, the development of humanity. The electricity sector is a major contributor to climate change because of its long-standing dependence on fossil fuels. However, the energy paradigm is changing, and renewable sources tend to play an increasingly important role in the energy mix in Portugal. Due to the strong relationship between renewable energies and climate-related natural resources, the climate change phenomenon could have considerable effects on the electricity sector. This paper analyzes the effects of climate change on the energy mix in Portugal in the medium / long term (up to 2050). The proposed methodology is based on the simulation of climate scenarios and projections of installed power by type and consumption. The combinations of these conditions are inputted to an energy accounting simulation tool, able to combine all information and provide a characterization of the system state for each case. The most favorable forecasted scenarios indicate that a fully renewable electricity system is achievable in the medium term, in line with the objectives of the European Union, as long as investments in renewable sources continue to be stimulated in the coming years.

Abstract
The building sector is responsible for a large share of Europe's energy consumption. Modelling buildings thermal behavior is a key factor for achieving the EU energy efficiency goals. Moreover, it can be used in load forecasting applications, for the prediction of buildings total energy consumption. The first phase of this work is the application of Artificial Immune Systems (AIS) for clustering buildings with similar physical characteristics and similar thermal efficiency. In the second phase, Artificial Neural Networks (ANN) are used to estimate the buildings heating and cooling loads. A final sensitivity test is performed to identify which building features have the most impact on the heating and cooling loads. The results obtained in the first phase revealed very distinct cluster prototypes, which demonstrates the AIS discriminating ability. The good estimation performance obtained in the second phase showed that this approach can be integrated in energy efficiency audits. Finally, the sensitivity analysis provided indications for actions (or legislation directives) in order to promote the design of more efficient buildings. © 2019 IEEE.