Transmission service operators (TSOs), regional security centres (RSCs), distribution service operators (DSOs), generation units (GUs) and balancing service providers (BSPs) need quick and reliable way of communication in order to secure power grid balance. They need to exchange information about grid stability, problems on the grid and defence plans in an easy and traceable way. In this paper we present software solution for handling these situations efficiently.

Numerous strategies were developed over the years in order to encourage users to reduce energy consumption and bolster energy efficiency. However, with increasing levels of efficiency achieved by most household appliances and high-consumption devices, one of the most impactful approaches that remains as a means to further increase energy efficiency is attempting to encourage users to behave in an energy-efficient manner. More precisely, positive behavior change can be motivated through the creation of unique social pressure and competition.

Intermittent renewable energy supply combined with electric and thermal energy storage technologies can cover the highest possible share of electricity, heating and cooling needs. However, their integration within the HVAC (Heating, Ventilation and Air-Conditioning) systems could result in far too complex installations, requiring intelligent energy management platforms for achieving their energy-efficient work.

Given the fact that renewable energy sources are increasing their share in the electricity market, in order to maintain the stable grid, i.e. match the production and the demand, it is crucial to have an accurate prediction of the expected accessible energy. Therefore, this paper is focused on providing the model for wind turbine production short-term forecast. The model is a deep neural network that includes LSTM, convolutional and dense layers, trained by the real-world data obtained from the wind farm in Krnovo, Montenegro.

The development of ICTs (Information and Communication Technologies) and the usage of personal data for both research and commercial purposes over the last years have brought the question of the protection of personal data. The GDPR (General Data Protection Regulation) has defined the ways how personal data should be treated, but the application of these requirements still remains an open issue.

Linked open data sources and the semantic web has become a precious data source for data analytics tasks and data integration. The growing data set sizes of RDF Knowledge Graph data need scalable processing and analytics techniques. The processing power of in-memory frameworks which can perform scalable distributed semantic analytics like SANSA, make use of Apache Spark and Apache Jena to provide start-to-end extensive scalable analytics on RDF knowledge graphs.

The last decades witnessed a significant evolution in terms of data generation, management, and maintenance, especially in the RDF format. Moreover, in the energy domain, semantic data is finding its way and can be used for various data