Given the increased dynamism and complexity of modern world, researchers struggle to cope to exploit as much as possible meaningful knowledge from the rapidly growing abundance of available data. The necessity for efficient representation of various interdependences among huge amounts of heterogeneous data, is emerging to be one of the most challenging tasks.
We propose development of a graph-based representation and accordingly learning framework for more efficient capturing of spatial and temporal dependences among datastreams. In fact, graphs enable richer and more effective representation of data and their relations giving more meaning to the available datasets, enabling in turn better efficiency in entire machine learning process and its application (including feature extraction and selection, classification, prediction etc.).
In particular, we consider Smart Grids, especially energy consumption as well as renewables generation forecasting. Improved accuracy of predictions in this field directly leads to optimization of Demand Side Management for all customers as well as assessment of renewables and EV insertion in specific areas leading to remarkable improvements in sustainability of cities. Integration of various heterogeneous energy consumption related data (i.e., electricity, gas and water consumption) and efficient capturing of their interdependences would be the very next step towards sustainability boosting urban development recommendation systems.