Data Capturing and Big Data analysis using multivariate statistical methods to maximize the healthy growth of animals, leading to higher quality levels
Capture data from numerous sources along the supply chain and make predictions about product quality, production sustainability, and consumer health impact; nimbly adopt advanced computational intelligence methods to improve competitive advances
Collecting energy consumption from laser metal cutting machines. In this case the goal is optimizing production plant energy consumption while reducing the waste of raw material
Collecting CO2 capture data from trees and soil using multiple advanced sensors to support credit certificates issuing for financial trading
Using intelligent energy management systems is dictated by the need to promote a renewable energy market within a local territory and to manage natural resources (e.g. water).
Future Energy Grids are subject to multiple fluctuations due to disparate causes and complexities, among which the environmental and climate effects, variations of economic nature, complexity and change of political and social nature. These considerations dictate the need for a robust and optimized predictive and adaptive management system, able to collect vast amounts of data and to adopt the most modern computational intelligent tools and techniques.
RECs are systemic organizational initiatives to manage the engineering, administrative, legal, economic and socio-cultural aspects of a territorial energy network of exchange and storage between local producers and consumers.
This emerging energy management model requires to face complexities which can only be handled by a robust, distributed, collaborative, intelligent IT platform.