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Highlights • Hourly-resolved model to investigate highly-renewable electricity generation in Spain. • Correlation analysis of time series to create 900 combinations used in simulations. • Transition paths evaluated based on security of supply, CO 2 emissions, and renewable share. • Short-term phase-out of nuclear and coal power plants proven to be feasible. Abstract Two alternative paths to achieve highly-renewable electricity generation in peninsular Spain are investigated in this paper. Every transition path comprises a description of the installed and decommissioned generation and storage capacities, from 2017 to 2030, as well as a hypothesis on the evolution of the electricity demand. The electricity mix for every hour within the transition path is determined through a dispatch algorithm that prioritizes electricity from renewable energy sources. The simulation is run for 900 different combinations of time series representing the hourly capacity factors of different technologies, as well as the electricity demand. This robust approach allows the evaluation of the transition paths based on the statistical distribution of several defined assessment criteria, such as security of supply, CO 2 emissions or renewable share in electricity generation. The feasibility of a Spanish power system with high renewable penetration is investigated not only in a future reference year but throughout the transition path. In particular, a progressive and simultaneous phase-out of nuclear and coal power plants in the short-term is proven to be feasible. Furthermore, the results sensitivity is analyzed including scenarios with a delayed nuclear phase-out, lower hydroelectricity generation due to more frequent and severe droughts caused by climate change and higher annual increment for the electricity demand. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an evolutionary algorithm for wind speed reconstruction from synoptic pressure patterns. The algorithm operates in a search space formed by grids of pressure measures, and must classify the different situations into classes, in such a way that a measure of wind speed in a given point is minimized among patterns assigned to the same class. Then, each class is assigned a mean wind speed and direction, so the wind speed reconstruction is possible for a new grid of synoptic pressures. In this paper we present the problem model and the specific description of the evolutionary algorithm proposed to solve the problem. We also show the good performance of the proposed method in the reconstruction of the average wind speed in six wind towers in Spain. The proposed method is applicable to wind speed reconstruction or reconstruction of wind missing data of wind series, specially when there is no other variable or related measure available. [Copyright &y& Elsevier]

Abstract: In recent years, fuzzy models have been acknowledged as a suitable approach for species distribution modelling due to their transparency and their ability to incorporate the ecological gradient theory. Specifically, the overlapping class boundaries of a fuzzy model are similar to the transitions between different environmental conditions. However, the need for ecological expert knowledge is an important constraint when applying fuzzy species distribution models. Moreover, the consistency of the ecological preferences of some fish species across different rivers has been widely contested. Recent research has shown that data-driven fuzzy models may solve this ‘knowledge acquisition bottleneck’ and this paper is a further contribution. The aim was to analyse the brown trout (Salmo trutta fario L.) habitat preferences based on a data-driven fuzzy modelling technique and to compare the resulting fuzzy models with a commonly applied modelling technique, Random Forests. A heuristic nearest ascent hill-climbing algorithm for fuzzy rule optimisation and Random Forests were applied to analyse the ecological preferences of brown trout in 93 mesohabitats. No significant differences in model performance were observed between the optimal fuzzy model and the Random Forests model and both approaches selected river width, the cover index and flow velocity as the most important variables describing brown trout habitat suitability. Further, the fuzzy model combined ecological relevance with reasonable interpretability, whereas the transparency of the Random Forests model was limited. This paper shows that fuzzy models may be a valid approach for species distribution modelling and that their performance is comparable to that of state-of-the-art modelling techniques like Random Forests. Fuzzy models could therefore be a valuable decision support tool for river managers and enhance communication between stakeholders. [Copyright &y& Elsevier]

Abstract: This paper describes an application of the Strength Pareto Evolutionary Algorithm to the multi-objective optimization of a stand-alone PV–wind–diesel system with batteries storage. The objectives to be minimized are the levelized cost of energy (LCOE) and the equivalent carbon dioxide (CO2) life cycle emissions (LCE). Each solution of the Pareto front is a possible solution for the PV–wind–diesel-batteries system, which can supply the load, but each one has a different LCOE and LCE. Some solutions have low LCOE values, but high LCE values, and vice versa. Results show that the photovoltaic (PV) generator is the most important source of electrical energy for stand-alone systems in Spain and Southern Europe, not only environmentally, but also economically. In some cases, PV is almost the only source of energy, as some solutions of the best Pareto front do not include wind turbines and diesel generators run only a few hours during the year. [Copyright &y& Elsevier]

Abstract: This paper presents a signal analysis methodology to validate (detect) and reconstruct the missing and false data of a large set of flow meters in the telecontrol system of a water distribution network. The proposed methodology is based on two time-scale forecasting models: a daily model based on a ARIMA time series, while the 10-min model is based on distributing the daily flow using a 10-min demand pattern. The demand patterns have been determined using two methods: correlation analysis and an unsupervised fuzzy logic classification, named LAMDA algorithm. Finally, the proposed methodology has been applied to the Barcelona water distribution network, providing very good results. [Copyright &y& Elsevier]

Abstract: This paper presents a decision support system for automatic keep-clear signs management. The system consists of several modules. First of all, an acquisition module obtains images using a vehicle equipped with two recording cameras. A recognition module, which is based on Support Vector Machines (SVMs), analyzes each image and decides if there is a keep-clear sign in it. The images with keep-clear signs are included into a Geographical Information System (GIS) database. Finally in the management module, the data in the GIS are compared with the council database in order to decide actions such as repairing or reposition of signs, detection of possible frauds etc. We present the first tests of the system in a Spanish city (Meco, Madrid), where the systems is being tested for its application in the near future. [Copyright &y& Elsevier]