Technology and the Internet have changed how travel is booked, the relationship between travelers and the tourism industry, and how tourists share their travel experiences. As a result of this multiplicity of options, mass tourism markets have been dispersing. But the global demand has not fallen; quite the contrary, it has increased. Another important factor, the digital transformation, is taking hold to reach new client profiles, especially the so‐called third generation of tourism consumers, digital natives who only understand the world through their online presence and who make the most of every one of its advantages. In this context, the digital platforms where users publish their impressions of tourism experiences are starting to carry more weight than the corporate content created by companies and brands. In this paper, we propose using different deep‐learning techniques and architectures to solve the problem of classifying the comments that tourists publish online and that new tourists use to decide how best to plan their trip. Specifically, in this paper, we propose a classifier to determine the sentiments reflected on the http://booking.com and http://tripadvisor.com platforms for the service received in hotels. We develop and compare various classifiers based on convolutional neural networks (CNN) and long short‐term memory networks (LSTM). These classifiers were trained and validated with data from hotels located on the island of Tenerife. An analysis of our findings shows that the most accurate and robust estimators are those based on LSTM recurrent neural networks.

One of today's greatest technological challenges is adding renewable energies to an electric grid, with the goal being to achieve sustainable and environmentally friendly electricity generation that is also affordable. In order for the incorporation of renewables to be successful, however, predictive tools are required which can be used to determine sufficiently far in advance how much renewable energy will be available to be injected into the grid so that the remaining generation sources, including those based on fossil fuels, can be adjusted in order to fill the demand. This would limit the environmental impact and the dependence on this type of fuel in a foreseeable shortfall scenario. This paper seeks to advance in the creation of these predictive generation models for wind farms using deep learning. We present a predictive model based on a deep, multi-layered neural network that based on a forecast for atmospheric conditions is capable of estimating the generation produced by a wind farm 24 h in advance. These models were trained and validated with data from a wind farm located on the island of Tenerife and show that the best of these predictors is more precise than the reference estimator and the prediction model currently used at the farm. We also note that the problem does not require models based on truly deep neural networks. However, the workflow for correctly developing, training, validating, and tuning these models is greatly enhanced by the advantages that deep learning techniques and tools can offer.

Making every component of an electrical system work in unison is being made more challenging by the increasing number of renewable energies used, the electrical output of which is difficult to determine beforehand. In Spain, the daily electricity market opens with a 12‐hour lead time, where the supply and demand expected for the following 24 hours are presented. When estimating the generation, energy sources like nuclear are highly stable, while peaking power plants can be run as necessary. Renewable energies, however, which should eventually replace peakers insofar as possible, are reliant on meteorological conditions. In this paper we propose using different deep‐learning techniques and architectures to solve the problem of predicting wind generation in order to participate in the daily market, by making predictions 12 and 36 hours in advance. We develop and compare various estimators based on feedforward, convolutional, and recurrent neural networks. These estimators were trained and validated with data from a wind farm located on the island of Tenerife. We show that the best candidates for each type are more precise than the reference estimator and the polynomial regression currently used at the wind farm. We also conduct a sensitivity analysis to determine which estimator type is most robust to perturbations. An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.