Integrated solutions based on neural networks for optimizing energy management in a microgrid

The article proposes the integration of an intelligent energy management system, in a microgrid with distributed production of energy from renewable sources (PD-RES), equipped with artificial intelligence elements (neural networks), capable of load forecasting on short tim horizon. In relation with identified or predicted situations, the decision support system that integrates these solutions, proposes, to consumer-producer (prosumer) of "green" energy, to act in a proactive manner for: reconfiguration of the microgrid's architecture, improving profits, and reducing the microgrid's vulnerability. From a practical point of view, the article results are based on data monitored form a three phase microgrid with 25kW installed power, equipped with energy storage elements, produced from renewable energy sources (wind and sun).