CI4EMS 2020 Abstracts

Area 1 - Computational Intelligence for Energy Management and Storage

Full Papers
Paper Nr: 3

Exogenous Data for Load Forecasting: A Review


Ramón Christen, Luca Mazzola, Alexander Denzler and Edy Portmann

Abstract: Electrical power load forecasting defines strategies for utilities, power producers and individuals that participate in a smart grid. While it is well established in planning processes for production and utilities, the importance of accurate forecasting increases for individuals. The ongoing deregulation of the electricity market enables energy trading by individuals, requiring an accurate estimation of the production and consumption. Research on forecast for aggregated demand shows that including features for the forecast from sources, called exogenous, additional to the purely historical consumption data allows to obtain higher accuracy. In fact, their usage demonstrated to be able to explain the large variability observed in the power demand, taking into account the individual influences. Anyway, the influence of exogenous data is hardly investigated for individual forecasting, due to the minor prevalence of this analysis to date. This review shows the benefit of exogenous data usage and the necessity of detailed research on the input features and their influence on detailed, individual level, forecasts of power demand. Eventually, this contribution is concluded by the presentation of open issues and research directions for electric smart communities that the authors would like to address.

Short Papers
Paper Nr: 1

Classification and Calibration Techniques in Predictive Maintenance: A Comparison between GMM and a Custom One-Class Classifier


Enrico de Santis, Antonino Capillo, Fabio F. Mascioli and Antonello Rizzi

Abstract: Modeling and predicting failures in the field of predictive maintenance is a challenging task. An important issue of an intelligent predictive maintenance system, exploited also for Condition Based Maintenance applications, is the failure probability estimation that can be found uncalibrated for most standard and custom classifiers grounded on Machine learning. In this paper are compared two classification techniques on a data set of faults collected in the real-world power grid that feeds the city of Rome, one based on a hybrid evolutionary-clustering technique, the other based on the well-known Gaussian Mixture Models setting. While the former adopts directly a custom-based weighted dissimilarity measure for facing unstructured and heterogeneous data, the latter needs a specific embedding technique step performed before the training procedure. Results show that both approaches reach good results with a different way of synthesizing a model of faults and with different structural complexities. Furthermore, besides the classification results, it is offered a comparison of the calibration status of the estimated probabilities of both classifiers, which can be a bottleneck for further applications and needs to be measured carefully.

Paper Nr: 2

Nanogrids: A Smart Way to Integrate Public Transportation Electric Vehicles into Smart Grids


Emanuele Ferrandino, Antonino Capillo, Fabio F. Mascioli and Antonello Rizzi

Abstract: The need for efficient integration of an Electric Vehicles (EVs) public transportation system into Smart Grids (SGs), has sparked the idea to equip them with Renewable Energy Systems (RESs), in order to reduce their impact on the SG. As a consequence, an EV can be seen as a Nanogrid (NG) whose energy flows are optimized by an Energy Management System (EMS). In this work, an EMS for an electric boat is synthesized by a Fuzzy Inference System-Hierarchical Genetic Algorithm (FIS-HGA). The electric boat follows cyclic routes day by day. Thus, single day training and test sets with a very short time step are chosen, with the aim of reducing the computational cost, without affecting accuracy. A convex optimization algorithm is applied for benchmark tests. Results show that the EMS succesfully performs the EV energy flows optimization. It is remarkable that the EMS achieves good performances when tested on different days than the one it has been trained on, further reducing the computational cost.