Abstract
This paper presents the study carried out in the Electric Power Systems Project (EPSP) of the Electrical Engineering course, by a student's team, using PBL, under a protocol between the Law Court and the Instituto Superior de Engenharia de Coimbra (ISEC / IPC). The purpose of the Electric Power Systems Project is to involve students in a project team, aiming to develop and test a system with a specific function, using concepts and technologies in the field of Electric Power Systems. Upon completing the course, students should demonstrate autonomy in identifying and analysing problems and propose, implement and test the specific solutions. In this work, the Project Based Learning (PBL) methodology was used. The Problem to be solved was presented to the students, by the Professors. The aim of the students was to provide a global framework for the problem of road lighting in Portugal, identifying the technologies used, existing legislation, national plans for improving energy efficiency in the sector, identify the most efficient technologies, learn how to work with software for simulation, propose a solution to the specific problem and justify the solution from a technical and economic point of view.

Abstract
We initiated this series with a view to catalyse and extend the focus on conceptualisation and application of behavioural science methods for managing innovation, albeit from a whole human perspective. We started with the notion that how to increase individual (human) creativity, collaboration productivity and innovativeness in innovation projects is a common concern for most firms. After discussions on the brain-mind-behaviour triad in the beginning, the interim editorial highlighted behavioural experiments as one plausible method to further the science of managing for innovation. In this final piece on the series, we conclude with a caveat on using experimental methods in examining the human side of innovation (Salampasis and Mention 2017) and discuss avenues for future research in innovation management, which increasingly reflects a collaborative affair (Bogers et al., 2017; Heil and Bornemann 2018). (...)

Abstract

Abstract
A grand challenge from the wind energy industry is to provide reliable forecasts on mountain winds several hours in advance at microscale (similar to 100 m) resolution. This requires better microscale wind-energy physics included in forecasting tools, for which field observations are imperative. While mesoscale (similar to 1 km) measurements abound, microscale processes are not monitored in practice nor do plentiful measurements exist at this scale. After a decade of preparation, a group of European and U.S. collaborators conducted a field campaign during 1 May-15 June 2017 in Vale Cobrao in central Portugal to delve into microscale processes in complex terrain. This valley is nestled within a parallel double ridge near the town of Perdigao with dominant wind climatology normal to the ridges, offering a nominally simple yet natural setting for fundamental studies. The dense instrument ensemble deployed covered a similar to 4 km x 4 km swath horizontally and similar to 10 km vertically, with measurement resolutions of tens of meters and seconds. Meteorological data were collected continuously, capturing multiscale flow interactions from synoptic to microscales, diurnal variability, thermal circulation, turbine wake and acoustics, waves, and turbulence. Particularly noteworthy are the extensiveness of the instrument array, space-time scales covered, use of leading-edge multiple-lidar technology alongside conventional tower and remote sensors, fruitful cross-Atlantic partnership, and adaptive management of the campaign. Preliminary data analysis uncovered interesting new phenomena. All data are being archived for public use.

Abstract
Day-ahead electricity price forecasting (DAEPF) plays a very important role in the decision-making optimization of electricity market participants, the dispatch control of independent system operators (ISOs) and the strategy formulation of energy trading. Unified modeling that only fits a single mapping relation between the historical data and future data usually produces larger errors because the different fluctuation patterns in electricity price data show different mapping relations. A daily pattern prediction (DPP) based classification modeling approach for DAEPF is proposed to solve this problem. The basic idea is that first recognize the price pattern of the next day from the "rough" day-ahead forecasting results provided by conventional forecasting methods and then perform classification modeling to further improve the forecasting accuracy through building a specific forecasting model for each pattern. The proposed approach consists of four steps. First, K-means is utilized to group all the historical daily electricity price curves into several clusters in order to assign each daily curve a pattern label for the training of the following daily pattern recognition (DPR) model and classification modeling. Second, a DPP model is proposed to recognize the price pattern of the next day from the forecasting results provided by multiple conventional forecasting methods. A weighted voting mechanism (WVM) method is proposed in this step to combine multiple day-ahead pattern predictions to obtain a more accurate DPP result. Third, the classification forecasting model of each different daily pattern can be established according to the clustering results in step 1. Fourth, the credibility of DPP result is checked to eventually determine whether the proposed classification DAEPF modeling approach can be adopted or not. A case study using the real electricity price data from the PJM market indicates that the proposed approach presents a better performance than unified modeling for a certain daily pattern whose DPP results show high reliability and accuracy.

Abstract
Building and maintaining complex systems requires good software engineering practices, including code modularity and reuse. The same applies in the context of coordination of complex component-based systems. This paper investigates how to verify properties of complex coordination patterns built hierarchically, i.e., built from composing blocks that are in turn built from smaller blocks. Most existing approaches to verify properties flatten these hierarchical models before the verification process, losing the hierarchical structure. We propose an approach to verify hierarchical models using containers as actions; more concretely, containers interacting with their neighbours. We present a dynamic modal logic tailored for hierarchical connectors, using Reo and Petri Nets to illustrate our approach. We realise our approach via a prototype implementation available online to verify hierarchical Reo connectors, encoding connectors and formulas into mCRL2 specifications and formulas. © 2019, IFIP International Federation for Information Processing.