Fiorella Lauro

• M. Sc. Control Engineering, PhD
• Fellow at Polytechnic University of Turin

The Active Demand Response (ADR), integrated with the distributed energy generation and storage systems, is the most common strategy for the optimization of energy consumption and indoor comfort in buildings, considering the energy availability and the balancing of the energy production from renewable sources. In the paper an overview of basic requ...

In recent years, Model Based Predictive Control (MPC) constitutes an important research trend in the control of advanced heating, ventilation and air conditioning (HVAC) systems in order to find a compromise between the energy savings and the occupant thermal comfort. In this paper an innovative MPC strategy for indoor temperature regulation in a t...

The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead forecasting of thermal comfort for office building heated by gas. Although the accuracy of the forecasting is similar for both the feed-forward and the recurrent network, the removal of features leads to accuracy reduction mu...

The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead fore- casting of gas consumption for office building heating. Although the accuracy of the forecasting is similar for both the feed-forward and the recurrent network, the removal of features leads to accuracy reduction much e...

In the building energy efficiency field, developing automatic and accurate fault detection and diagnosis methods is necessary in order to ensure optimal operations of systems and to save energy. In this paper first, fault detection analysis based on statistical methods where anomalies are detected through a comparison with neighborhood and averaged...

In this paper a fault detection analysis through a neural networks ensembling approach and statistical pattern recognition techniques is presented. Abnormal consumption or faults are detected by analyzing the residual values, which are the difference between the expected and the real operating data. The residuals are more sensitive to faults and in...

In this paper, an innovative and automated fault detection and diagnosis (FDD) approach based on high-level correlation rules in order to improve reliability, safety and efficiency of a supervised building is presented. The proposed method is based on the data fusion of different measurements, using their fuzzification and aggregation through suita...

In the proposed work we aim at modelling building lighting energy consumption. We compared several classical methods to the latest Artificial Intelligence modelling technique : Artificial Neural Networks Ensembling (ANNE). Therefore, in this study we show how we built the ANNE and a new hybrid model based on the statistical-ANNE combination. Experi...

L’attività condotta dal gruppo di ricerca del Politecnico di Torino si è declinata secondo diverse fasi e obiettivi finalizzati all’individuazione di metodologie innovative per la diagnostica degli edifici. Il lavoro di ricerca ha comportato dapprima l’esigenza di condurre un’analisi critica e strutturata circa il sistema di monitoraggio installato...

In the paper a fault detection analysis through neural ensembling approaches is presented. Experimentation was carried out over two months monitoring data sets for the lighting energy consumption of an actual office building located at ENEA ‘Casaccia’ Research Centre. Using a fault free data set for the training, the Artificial Neural Networks Ense...