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The increased penetration of renewable energy sources (RESs) as an effective mechanism to reduce carbon emissions leads to an increased weather dependency for power and energy systems. This has created dynamic operation and degradation phenomena, which affect the lifetime estimation of the assets operated with RESs. For the reliable and efficient operation of RES it is crucial to monitor the health of its constituent components and feature selection is a crucial step for building robust and accurate health monitoring approaches. In this context, this paper presents a probabilistic feature selection approach, which probabilistically weights and selects features through a heuristic and iterative process for an improved asset lifetime estimation. Power transformers are key power grid assets and they are used to demonstrate the validity and impact of the proposed approach. The approach is tested on two different photovoltaic power plants operated in Spain and Australia. Results consistently show that the proposed feature-selection approach reduces the prediction error and consistently selects relevant features. The approach has been applied to transformer lifetime estimation, but it can be generally applied to assist in the lifetime estimation of other components operated in RESs. Part of the studies presented here as well as source codes are all open-source under the GitHub repository https://github.com/iramirezg/FeatureSelection. • Probabilistic feature selection approach for improved asset lifetime estimation. • Integration of environmental features for improved renewable-operated asset lifetime. • Systematic and robust feature weighting methodology. • Improved transformer lifetime estimation including sensor and environmental data. • Validated on two real photovoltaic power plant case studies. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reducing the number of values required to characterize an electricity demand profile, which is usually known as its dimensionality. This reduction may have a significant impact on the computational efforts and storage capacities required to analyze and process high volumes of electricity load curves. Also, the reduction to 2 or even 1 component enables its graphic representation. Specifically, this work is mainly focused on profiles defined by their monthly demand values, and where the clients are aggregated by locations and/or economic activities. This approach is of great interest for marketing analysis and decision-making of electricity retailers. In this sense, the use of dimensionality reduction techniques based on knowledge (calendar and temperature) along with the application of data-driven procedures (Principal Component Analysis and autoencoders), are explored in the paper. The results of this research show that autoencoders clearly outperform the other techniques, yielding errors in the reduction process between 15% to 40% lower and preserving distances between profiles in the low-dimensional spaces, with a correlation of 0.93 with the distances in high dimensional space. Additionally, the bidimensional graphical representation of a profile can easily be interpreted in a polar way, where the angle denotes the shape of the profile, and the radius reveals its scale. To reach these results, a very large dataset has been employed, with about half a million aggregated profiles corresponding to the electricity consumption during 3 years of more than 27 million clients in Spain. [Display omitted] • Autoencoders are the best option to reduce the dimensionality of demand profiles. • Data-driven techniques outperform knowledge-based models by reducing components. • Low-dimensional characterization preserves the relative distances between profiles. • The graphic representation of bidimensional profiles admits a polar interpretation. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, next-day hourly forecasts are calculated for the energy price in the electricity production market of Spain. The methodology used to achieve these forecasts is based on artificial neural networks, which have been used successfully in recent years in many forecasting applications. The days to be forecast include working days as well as weekends and holidays, due to the fact that energy price has different behaviours depending on the kind of day to be forecast. Besides, energy price time series are usually composed of too many data, which could be a problem if we are looking for a short period of time to reach an adequate forecast. In this paper, a training method for artificial neural nets is proposed, which is based on making a previous selection for the multilayer perceptron (MLP) training samples, using an ART-type neural network. The MLP is then trained and finally used to calculate forecasts. These forecasts are compared to those obtained from the well-known Box–Jenkins ARIMA forecasting method. Results show that neural nets perform better than ARIMA models, especially for weekends and holidays. Both methodologies calculate more accurate forecasts—in terms of mean absolute percentage error—for working days that for weekends and holidays. Agents involved in the electricity production market, who may need fast forecasts for the price of electricity, would benefit from the results of this study. [Copyright &y& Elsevier]

In this paper we propose a novel computational system for simultaneous modelling and prediction of rainfall occurrence and amount. The proposed system is based on a hierarchical system of nominal–ordinal support vector classifiers, the former focussed on the prediction of the rainfall occurrence, and the latter centered in the expected rainfall amount from a set of three different ordinal classes. In addition to the proposed model, we use a novel set of predictive meteorological variables, which improve the classifiers performance in this problem. We evaluate the proposed system in a real problem of rainfall forecast at Santiago de Compostela airport, Spain, showing that the system is able to obtain an accurate prediction of occurrence and rainfall amount, and we discuss the usefulness of the proposed system as part of the airport weather forecast and warning system, in order to improve airport operations. [ABSTRACT FROM AUTHOR]

Abstract: SigTur/E-Destination is a Web-based system that provides personalized recommendations of touristic activities in the region of Tarragona. The activities are properly classified and labeled according to a specific ontology, which guides the reasoning process. The recommender takes into account many different kinds of data: demographic information, travel motivations, the actions of the user on the system, the ratings provided by the user, the opinions of users with similar demographic characteristics or similar tastes, etc. The system has been fully designed and implemented in the Science and Technology Park of Tourism and Leisure. The paper presents a numerical evaluation of the correlation between the recommendations and the user''s motivations, and a qualitative evaluation performed by end users. [Copyright &y& Elsevier]