The energy sector contributes approximately two-thirds of global greenhouse gas emissions. In this context, the sector must adapt to new supply and demand networks for all future energy sources. The ongoing transformation in the European energy field is driven by the ambition of the European Union to reach the climate objectives set for 2030. The main actions are increasing renewable energy production, adapting transition fuels like natural gas to reduce emissions, improving energy efficiency across all economic sectors, prioritizing building, transportation, (...) and industry, developing Carbon Capture and Storage technologies, and ensuring universal access to clean and affordable energy. The significant changes envisaged in the energy sector to increase renewable energy production and consumption require improved integration and more use of predictive tools to support stakeholders' decision-making processes. This article presents a case study to assess the performance of predictive models based on a Feed-Forward Neural Network architecture that employ Root Mean Squared Propagation as their optimization function in terms of choosing the most appropriate activation function for this kind of data input and output. The training and testing phases of the models use data about building energy consumption. The lowest training time and Root Mean Square Error values were similar for Rectified Linear Unit and Tanh activation functions. The model with the Rectified linear Unit activation function performed best. (shrink)

In this study there are presented a series of aspects concerning the religious behavior of the Romanian population. By using quantitative analysis we have identified a series of religious key factors which can be used by parties and politicians to obtain votes during election campaigns. The obtained results highlight the fact that people with a lower level of education are more receptive to the political messages transmitted by religious means. Also, the influence of political actions and messages having a religious (...) character is more powerful at the level of the population with a lower standard of living. By means of quantitative analysis we have also evaluated the impact of religious and ethnical fractionalization on the political representation in the national parliament. (shrink)

Telecommunications is one of the most dynamic sectors in the market, where the customer base is an important pawn in receive safe revenues, so is important to focus attention is paid to maintaining them with an active status. Migrating customers from one network to another varies among telecommunication companies depending on different factors such as call quality, pricing plan, minute consumption, data, sms facilities, customer billing issues, etc. Determining an effective predictive model helps detect early warning signals when churn occurs (...) and assigns to each customer a score called “churn score” that indicates the likelihood that the individual might migrate to another network over a predefined time period. To this extent, the present paper uses more than 10k customers sample of a telecommunication company and tries to analyse the churn behavior. The aim of the paper is both to test the efficiency and performance of the most commonly used data mining techniques to predict the churn behavior and to underline the main indicators that can be used when conducting such analyses. Knowing the magnitude of the churn phenomenon, the company can prevent the instability that is going to occur by applying a series of measure in order to increase the retention of the current customers. (shrink)