Abstract
This paper proposes a case-based reasoning methodology to automatically choose the most appropriate optimization algorithms and respective parameterizations to solve the problem of optimal resource scheduling in smart energy grids. The optimal resource scheduling is, however, a heavy computation problem, which deals with a large number of variables. Moreover, depending on the time horizon of this optimization, fast response times are usually required, which makes it impossible to apply traditional exact optimization methods. For this reason, the application of metaheuristic methods is the natural solution, providing near-optimal solutions in a much faster execution time. Choosing which optimization approaches to apply in each time is the focus of this work, considering the requirements for each problem and the information of previous executions. A case-based reasoning methodology is proposed, considering previous cases of execution of different optimization approaches for different problems. A fuzzy logic approach is used to adapt the solutions considering the balance between execution time and quality of results Copyright © 2017 for this paper by its authors.

Abstract
While most physical systems or phenomena occur in continuous-time, identification methods based on discrete-time models are more widespread among practitioners and academic community, possibly due to the discrete-time nature of the data records. There has been a growing interest in estimating continuous-time (CT) models in the last decade. This work develops algorithms to estimate the parameters of multivariable state-space CT models from input-output samples using a method based on the recently developed MOLI-ZOFT approach. The performance of the algorithm is evaluated using real data from an industrial winding process.

Abstract
A major rehabilitation programme of secondary school buildings has been carried out in the last few years in Portugal. With the introduction of HVAC systems in buildings that were previously naturally ventilated, an increase on energy consumption has been verified. During the first occupancy periods of new and refurbished buildings, energy and indoor climate quality audits are important strategies to improve the buildings’ energy use. In this context, this paper aims at showing the relations between the energy consumption and indoor environment quality (IEQ) parameters, obtained from the energy and IEQ audit in six representative modernised secondary schools – part of a larger R&D project untitled 3Es – geographically and climatically distributed in Portugal mainland. The monitoring period during the mid-season 2013 varied between schools, between two and three weeks. Air exchange rates, more specifically infiltration rates, were quantified aiming at determining the current airtightness condition of the refurbished schools. A subjective IEQ assessment was also performed, focusing on occupants' feedback, providing insight on the potential linkages between energy use and occupants’ comfort. A reflection on the energy consumption indicators and the indoor conditions obtained in the classrooms was proposed, and some suggestions were anticipated.

Abstract
Social network analysis (SNA) is a widely studied research topics, which has been increasingly being applied for solving different kind of problems, including industrial manufacturing ones. This paper focuses on the application of SNA on an industrial plant layout problem. The study aims at analyzing the importance of using SNA techniques to analyze important relations between entities in a manufacturing environment, such as jobs and resources in the context of industrial plant layout analysis. The study carried out enabled to obtain relevant results for the identification of relations among these entities for supporting to establish an appropriate plant layout for producing the jobs.

Abstract
Electricity consumption has increased all around the world in the last decades. This has caused a rise in the use of fossil fuels and in the harming of the environment. In the past years the use of renewable energies and reduction of consumption has growth in order to deal with that problem. The change in the production paradigm led to an increasing search of ways to shorten consumption and adapt to the production. One of the solutions for this problem is to use Demand Response systems. Lighting systems have a major role in electricity consumption, so they are very suitable to be applied in a Demand Response system, optimizing their use. This optimization can be made in different ways being one of them by using a heuristic algorithm. This paper focuses on the use of Fish School Search algorithm to optimize a lighting system, in order to understand its capability of dealing with a problem of this nature and compare it with other algorithms to evaluate its performance.

Abstract
This paper presents the timing analysis methodology developed in the European project P-SOCRATES (Parallel Software Framework for Time-Critical Many-core Systems). This timing analysis methodology is defined for parallel applications that must satisfy both performance and real-time requirements and are executed on modern many-core processor architectures. We discuss the motivation and objectives of the project, the timing analysis flow that we proposed, the tool that has been developed to automatize it, and finally we report on some of the preliminary results that we have obtained when applying this methodology to the three application use-cases of the project.