An effective energy oversight represents a major concern throughout the world, and the problem has become even more stringent recently. The prediction of energy load and consumption depends on various factors such as temperature, plugged load, etc. The machine learning and deep learning (DL) approaches developed in the last decade provide a very high level of accuracy for various types of applications, including time-series forecasting. Accordingly, the number of prediction models for this task is continuously growing. The current study does not only overview the most recent and relevant DL for energy supply and demand, but it also emphasizes the fact that not many recent methods use parameter tuning for enhancing the results. To fill the abovementioned gap, in the research conducted for the purpose of this manuscript, a canonical and straightforward long short-term memory (LSTM) DL model for electricity load is developed and tuned for multivariate time-series forecasting. One open dataset from Europe is used as a benchmark, and the performance of LSTM models for a one-step-ahead prediction is evaluated. Reported results can be used as a benchmark for hybrid LSTM-optimization approaches for multivariate energy time-series forecasting in power systems. The current work highlights that parameter tuning leads to better results when using metaheuristics for this purpose in all cases: while grid search achieves a coefficient of determination ($R^2$) of 0.9136, the metaheuristic that led to the worst result is still notably better with the corresponding score of 0.9515.

As solar energy generation has become more and more important for the economies of numerous countries in the last couple of decades, it is highly important to build accurate models for forecasting the amount of green energy that will be produced. Numerous recurrent deep learning approaches, mainly based on long short-term memory (LSTM), are proposed for dealing with such problems, but the most accurate models may differ from one test case to another with respect to architecture and hyperparameters. In the current study, the use of an LSTM and a bidirectional LSTM (BiLSTM) is proposed for dealing with a data collection that, besides the time series values denoting the solar energy generation, also comprises corresponding information about the weather. The proposed research additionally endows the models with hyperparameter tuning by means of an enhanced version of a recently proposed metaheuristic, the reptile search algorithm (RSA). The output of the proposed tuned recurrent neural network models is compared to the ones of several other state-of-the-art metaheuristic optimization approaches that are applied for the same task, using the same experimental setup, and the obtained results indicate the proposed approach as the better alternative. Moreover, the best recurrent model achieved the best results with $R^2$ of 0.604, and a normalized MSE value of 0.014, which yields an improvement of around 13% over traditional machine learning models.

Cities are multifunctional by definition, and an increasingly significant function is the tourist function. City tourism is one of the most dynamically developing forms of tourism. Tourists’ decisions regarding choosing a destination are influenced by a number of factors determining the subjective assessment of the tourist attractiveness of a given city, and one of them may be the state of air pollution, as it can have a negative impact on the health of both city dwellers and tourists. This article is an attempt to determine whether potential tourists consider information about the level of a city’s air quality in the assessment of its tourist attractiveness and the impact of this information on their travel decisions. The article presents the results of surveys conducted among a group of 509 respondents from Poland. On this basis, an assessment was made of the extent to which information on the condition of air quality in a given city is relevant for persons planning a tourist trip. In the conducted research, decisions regarding both business and private trips were evaluated. In addition, information on factors that could increase the respondents’ interest in the condition of air quality in the city of the intended trip (e.g., trip with children, trip length) was collected. Due to the fact that tourism is a significant source of income for many cities, the research results presented in the article may be of significant importance for entities creating the urban tourist product and responsible for its management. The article also draws attention to the fact that reducing pollution in cities can contribute to increases in their tourist attractiveness.