Filipe Tadeu Oliveira

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.

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
An AC arc furnace is an unbalanced, non-linear and time-varying load, which can cause many power quality problems to the electric network inside the plant and in its electrical vicinity. Although different studies addressing harmonics and flicker analyses on arc furnaces can be found in the bibliography, it is very difficult obtain an exact model that considers all the parameters that influence the process, and therefore it is necessary to obtain actual measurements under different conditions. This paper presents measurement results for harmonic distortion, flicker and unbalance obtained over three different measurement campaigns on an iron and steel industry (SNL), as well as the pertinent conclusions. Measurement campaigns were performed on an AC arc furnace of 83 MW (170 TM) with a 120 MVA transformer connected by a ‘dirty’ 220 kV line (55 km) to the Substation of Carregado, where other feeders supply industrial and domestic consumers. Finally, the dynamic behaviour of an SVC will be analysed and compared to that of a STATCOM by means of simulation studies.

Abstract
The main objective of this paper consists in presenting a survey of voltage sags and short interruptions in different places along the geography of Galicia measured throughout two years. Effects and the possible solutions of the voltage sags and short interruptions in one industrial installation, PSA-Vigo, that produce cars, are also addressed. Finally, we will establish the corresponding conclusions.

Abstract