1,721,009 research outputs found
Building energy management through fault detection analysis using pattern recognition techniques applied on residual neural networks
No full textIn 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 insensitive to noise. In this study, first, the experimentation is carried out over two months monitoring data set for the lighting energy consumption of an actual office building. Using a fault free data set for the training, an artificial neural networks ensemble (ANNE) is used for the estimation of hourly lighting energy consumption in normal operational conditions. The fault detection is performed through the analysis of the magnitude of residuals using peak outliers detection method. Second, the fault detection analysis is also carried out through statistical pattern recognition techniques on structured residuals of lighting power consumption considering different influencing attributes i.e. number of people, global solar radiation etc. Moreover the results obtained from these methods are compared to minimize the false anomalies and to improve the FDD process. Experimental results show the effectiveness of the ensembling approach in automatic detection of abnormal building lighting energy consumption. The results also indicate that statistical pattern recognition techniques applied to residuals are useful for detecting and isolating the faults as well as noise
A control strategy for district energy management
No full textThis paper deals with the District Energy Management problem by proposing the hierarchical control architecture and a suitable control strategy for the energy governance of a district of public office buildings. The District Energy Management System (DEMS) provides energy for the building network on the basis of the Day-Ahead Energy Market rules. Moreover, it monitors the district power consumption and interacts with the Building Energy Management Systems (BEMS) to optimize the power consumption the day-ahead, by respecting the user's comfort preferences and minimizing the costs. Two optimization models are formulated for both building and district energy management. The first optimization is performed by the BEMS and pursues the objective of satisfying the thermal comfort by minimizing the power cost function. The second optimization is performed by the DEMS in order to minimize the district power cost by appropriately balancing the penalties and rewards. © 2015 IEEE
Low Order Grey-box Models for Short-term Thermal Behavior Prediction in Buildings
No full textLow order grey-box models are suitable to be used in predictive controls. In real buildings in which the measured quantities are few the reliability of these models is crucial for the control performance. In this paper an identification procedure is analyzed to investigate the accuracy of different order grey-box models for short-term thermal behavior prediction in a real building, part of a living smart district. The building has a low number of zones and a single indoor temperature measuring point. The models are identified on the data acquired in 31 days during the winter 2015. The second order model shows the best performance with a root-mean-square error (RMSE) less than 0.5°C for a prediction horizon of 1-hour and a RMSE less than 1°C for a prediction horizon of 3-hours. © 2017 The Authors
Adaptive Street Lighting Predictive Control
No full textIn this paper an implementation of a smart predictive monitoring and adaptive control system for the public lighting have been carried out. The vehicular traffic flow acquired using a smart camera has been analyzed and several predictive methods have been studied. Then, a control strategy based on the given traffic forecasts and on the dynamical street class downgrade allowed by the law, has been implemented. Experimental results provided by a real life testbed showed that the proposed strategy has high potential energy savings without affecting safety. © 2017 The Authors
Urban traffic flow forecasting using neural-statistic hybrid modeling
No full textIn this paper we show a hybrid modeling approach which combines Artificial Neural Networks and a simple statistical approach in order to provide a one hour forecast of urban traffic flow rates. Experimentation has been carried out on three different classes of real streets and results show that the proposed approach clearly outperforms the best of the methods it combines. © 2013 Springer-Verlag
Indoor lighting fault detection and diagnosis using a data fusion approach
No full textIn 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 suitable operators, in order to get dimensionless severity indicators able to diagnose faults and to identify the possible causes (ranked according their severity) generating them. Thus, a set of possible anomalies that can occur in a building and the correlation with measured physical quantities were identified. Experimentation of this FDD technique was applied to indoor lighting of a real office building. The proposed method was validated over a onemonth period with the aim of detecting anomalous consumption events, considering when and in which circumstances they occurred. After this stage, the FDD system was performed in real time operation. © 2014 WIT Press
Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling
No full textIn this paper we show a hybrid modeling approach which combines Artificial Neural Networks and a simple statistical approach in order to provide a one hour forecast of urban traffic flow rates. Experimentation has been carried out on three different classes of real streets and results show that the proposed approach outperforms the best of the methods it puts together. © 2015 Elsevier B.V
Building energy consumption modeling with Neural Ensembling Approaches for fault detection analysis
No full textIn 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 Ensembling (ANNE) were used for the estimation of hourly lighting energy consumption in normal operational conditions. The fault detection was performed through the analysis of the magnitude of residuals using a peak detection method. Moreover the peak detection method was applied directly to the testing data set. Finally a majority voting method to ensemble the results of different ANN classifiers was performed. Experimental results show the effectiveness of ensembling approaches in automatic detection of abnormal building lighting energy consumptio
Smart street lighting management
No full textIn this work, we propose a new street lighting energy management system in order to reduce energy consumption. The key idea we want to accomplish is that of "energy on demand" meaning that energy, in this case light, is provided only when needed. In order to achieve this goal, it is critical to have a reliable demand model, which in the case of street lighting turns out to be a traffic flow rate forecasting model. In order to achieve this goal, several methods on the 1-h prediction have been compared and the one providing the best results is based on artificial neural networks. Moreover, several control strategies have been tested and the one which gave the best energy savings is the adaptive one we carried out. Experimentation has been carried out on real data and the study shows that with the proposed approach, it is possible to save up to 50 % of energy compared to no regulation systems. © 2013 Springer Science+Business Media Dordrecht
Città sostenibili e smart cities: partecipazione a network nazionali ed internazionali
No full textIl network JP Smart Cities ha lo scopo di mappare le iniziative europee sulla smart city; l’ENEA è il delegato nazionale italiano e coordinatore di uno dei quattro sub-programme.
Il network JPI Urban Europe ha come obiettivo lo sviluppo - a livello europeo - di ricerche coordinate che, con iniziative innovative di lungo respiro (2050), siano in grado di contribuire alla definizione di nuovi modelli urbani in tempi di cambiamento globale
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