About the pilot

The pilot focuses on gathering relevant and valid data sources, such as weather, buildings’ characteristics and energy consumption, and with the aid of AI and machine learning algorithms detect patterns and problems regarding energy efficiency, so that possible data-driven policies can be defined to ensure a more sustainable and efficient environment in the city. Data regarding energy consumption will be gathered using smart meters and IoT devices. Apart from the current energy consumption time interval of 15 minutes, with the implementation of IoT devices, a deeper analysis will be made, to analyse specific types of energy consumption in each building, assuming a breakdown of consumption considering the equipment existing in each delivery point. Data regarding buildings archetypes is gathered with the support from other projects of LIS. With these main data sources, AI models can be implemented to analyse patterns and support policy making on general energy performance improvement. Deep Learning models such as long/short-term memory network (LSTM Network), Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs) can also be used to provide forecast on energy consumption. Clustering algorithms, e.g., k-Means, Hierarchical Cluster Analysis and Expectation Maximization can be used to group clients on similar energy usage.

Pilot Poster