There is a need for more energy system integration, while the autonomy of individual systems is necessary to cope with the exploding complexity of multiple buildings and their interaction with the electricity grid. The use of Big Data in combination with deep learning techniques offers new opportunities to better predict energy consumption and decentralized production of renewable energy (for example, based on local weather data taking into account local phenomena such as urban heat islands). This combined with multi-agent systems with a cooperative approach provides decentralized control and monitoring autonomy to further reduce the complexity of energy system integration.