Building lighting energy consumption modelling with hybrid neural-statistic approaches

"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. Experimentation has been carried out over a three. months data set coming from a real office building located in the ENEA ‘Casaccia’. Research Centre. Experimental results show that the proposed hybrid statistical-ANNE approach can get a remarkable improvement with respect to the best classical method(the statistical one).

Model Predictive Control for Building Active Demand Response Systems

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 requirements and applications of ADR management is presented. Specifically, the model predictive control (MPC) adopted in several applications as optimal control strategy in the ADR buildings context is analysed. Finally the research experience of the authors in this context is described

BRCA1 and BRCA2 mutations in central and southern Italian patients

INTRODUCTION: Germline BRCA1 and BRCA2 mutations account for most hereditary breast/ovarian cancers and are associated with male breast cancer. Furthermore, constitutional mutations in these genes may occur in breast/ovarian cancer patients that do not meet stringent criteria of autosomal-dominant predisposition. The relevance of BRCA1 and BRCA2 mutations in such patients is still debated. OBJECTIVES: We sought to determine the impact of BRCA1 and BRCA2 mutations in a population of patients from central and southern Italy. We analyzed the BRCA1 and BRCA2 coding regions in 136 unrelated probands: 117 females with breast/ovarian cancer and 19 males with breast cancer. This population of patients was mostly representative of cases who are at risk for hereditary susceptibility, but who do not meet stringent criteria of autosomal-dominant predisposition. METHODS: Probands, subclassified as follows, were consecutively recruited depending on informed consent from patients attending breast cancer clinics in Rome and Naples. Selection criteria for females were as follows: breast cancer with breast cancer family history [one to two first-/second-degree relative(s), n = 55]; breast cancer diagnosed before age 40 years (no breast/ovarian cancer family history, n = 28); bilateral breast cancer (regardless of age and family history, n =10); breast cancer associated with gastrointestinal, pancreatic or uterine cancers [synchronous/metachronous or in first-degree relative(s), n = 9]; breast or ovarian cancer with family history of breast-ovarian/ovarian cancer (at least 1 first-/ second-degree relative, n = 10); and ovarian cancer with no breast/ovarian cancer family history (n = 5). Males with breast cancer were recruited regardless of age and family history. BRCA1 exon 11 and BRCA2 exons 10 and 11 were screened by PTT. Coding BRCA1 exons 2, 3, 5-10 and 12-24 and BRCA2 exons 2-9 and 12-27 were screened by SSCP. Primers are listed in Table 1. In 27 cases, analyzed by PTT along the entire BRCA1 coding sequence, BRCA1 SSCP analysis was limited to exons 2, 5, 20 and 24. Mutations were verified by sequence analysis on two independent blood samples. RESULTS: Deleterious germline BRCA1/BRCA2 mutations were detected in 11 out of 136 cases (8%). Only three BRCA2 mutations were novel. One BRCA2 mutation recurred in two unrelated probands. Table 2 shows the mutations and data concerning carriers and their families. Table 3 shows correlations between BRCA1/BRCA2 mutations and sex, age at disease diagnosis and familial clustering of breast/ovarian cancer in the total patient population. Table 4 shows the proportions of BRCA1 and BRCA2 mutations in females with site-specific breast and breast-ovarian/ovarian cancer. Table 5 shows the frequency of BRCA1/BRCA2 mutations in males. BRCA1 and BRCA2 mutations, respectively, accounted for four out of 68 (6%) and one out of 68 (1%) cases diagnosed before age 50 years, and for one out of 68 (1%) and five out of 68 (7%) cases diagnosed after age 50 years. BRCA1 mutations were found in five out of 117 females (4%) and in none of 19 males (0%), and BRCA2 mutations were found in four out of 117 females (3%) and in two out of 19 males (10%). The proportions of BRCA1 and BRCA2 mutations coincided in site-specific female breast cancers (four out of 102; ie 4% each). BRCA1 and BRCA2 equally contributed to female breast cancers, with no familial clustering in those diagnosed before age 40 years (one out of 28; 4% each), and to female breast cancers, all ages, with familial clustering in one to two relatives (three out of 55; ie 5% each). In the latter subset of cases, BRCA1 mostly accounted for tumours diagnosed before age 40 years (two out of eight; 25%), and BRCA2 for tumours diagnosed after age 50 years (three out of 34; 9%). Regardless of family history, the respective contributions of BRCA1 and BRCA2 to site-specific female breast cancers diagnosed before age 40 years were 8% (three out of 36) and 3% (one out of 36). One BRCA1 mutation was detected among the 15 female probands from breast-ovarian/ovarian cancer families (7%). Among male breast cancers, BRCA2 mutations were identified in one out of five (20%) cases with family history and in one out of 14 (7%) apparently sporadic cases. No BRCA1 or BRCA2 mutations were found in female probands with nonfamilial bilateral breast cancer (10 cases) or in those with breast cancer associated with gastrointestinal, pancreatic or uterine cancers, synchronous/metachronous or in first-degree relative(s) (nine cases). These cases were all diagnosed after age 40 years. DISCUSSION: Our results indicate a lack of relevant founder effects for BRCA1- and BRCA2-related disease in the sample of patients studied, which is consistent with other Italian studies and with ethnical and historical data. Overall, the contribution of BRCA1 and BRCA2 to breast/ovarian cancer in Italian patients appears to be less significant than in patients from communities with founder mutations. The present study is in agreement with direct estimates on other outbred populations, indicating that 7-10% of all female breast cancers that occur in patients aged under 40 years are due to BRCA1/BRCA2. We found that BRCA1 and BRCA2 equally contributed to site-specific breast cancers who had one/two breast cancer-affected first-/second-degree relative(s) or who were diagnosed within age 40 years in the absence of family history. This is consistent with recent data that indicated that the respective frequencies of BRCA1 and BRCA2 mutations are comparable in early onset breast cancer. Considering the total population of patients analyzed here, however, BRCA1 and BRCA2 mutations were mostly found in cases with disease diagnosis before and after age 50 years, respectively. Moreover, in cases with familial clustering of site-specific breast cancer, BRCA1 mostly accounted for tumours diagnosed before age 40 years, and BRCA2 for tumours diagnosed after age 50 years. This is in agreement with a trend, which has been observed in other populations, for the proportion of cases with BRCA2 mutations to increase, and the proportion with mutations in BRCA1 to decrease, as the age at cancer onset increases. As in other studies, the frequency of BRCA1/BRCA2 mutations taken together was lower than the estimated frequencies at comparable ages for all susceptibility alleles derived from the Contraceptive and Steroid Hormones (CASH) study. The discrepancy between direct data deriving from BRCA1/BRCA2 mutational analysis and CASH estimates could be due to several factors, including contribution of gene(s) other than BRCA1/BRCA2, differences between populations and relative insensitivity of mutational screening. Only BRCA1 mutations were found in breast/ovarian and site-specific ovarian cancer families. BRCA2, but not BRCA1 mutations were found in the male breast cancers. The overall proportion of males with BRCA2 mutations was high when compared with data from other studies on outbred populations, but was low compared with data from populations with founder effects. The present results should be regarded as an approximation, because the following types of mutation are predicted to escape detection by the screening strategy used: mutations in noncoding regions; missense mutations within BRCA1 exon 11 and BRCA2 exons 10 and 11; large gene deletions; and mutations within the first and last 180 nucleotides of the amplicons analyzed by PTT

Fault detection analysis using data mining techniques for a cluster of smart office buildings

There is an increasing need for automated fault detection tools in buildings. The total energy request in buildings can be significantly reduced by detecting abnormal consumption effectively. Numerous models are used to tackle this problem but either they are very complex and mostly applicable to components level, or they cannot be adopted for different buildings and equipment. In this study a simplified approach to automatically detect anomalies in building energy consumption based on actual recorded data of active electrical power for lighting and total active electrical power of a cluster of eight buildings is presented. The proposed methodology uses statistical pattern recognition techniques and artificial neural ensembling networks coupled with outliers detection methods for fault detection. The results show the usefulness of this data analysis approach in automatic fault detection by reducing the number of false anomalies. The method allows to identify patterns of faults occurring in a cluster of bindings; in this way the energy consumption can be further optimized also through the building management staff by informing occupants of their energy usage and educating them to be proactive in their energy consumption. Finally, in the context of smart buildings, the common detected outliers in the cluster of buildings demonstrate that the management of a smart district can be operated with the whole buildings cluster approach

Building energy consumption modeling with Neural Ensembling Approaches for fault detection analysis

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 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

Fault detection analysis using data mining techniques for a cluster of smart office buildings

There is an increasing need for automated fault detection tools in buildings. The total energy request in buildings can be significantly reduced by detecting abnormal consumption effectively. Numerous models are used to tackle this problem but either they are very complex and mostly applicable to components level, or they cannot be adopted for different buildings and equipment. In this study a simplified approach to automatically detect anomalies in building energy consumption based on actual recorded data of active electrical power for lighting and total active electrical power of a cluster of eight buildings is presented. The proposed methodology uses statistical pattern recognition techniques and artificial neural ensembling networks coupled with outliers detection methods for fault detection. The results show the usefulness of this data analysis approach in automatic fault detection by reducing the number of false anomalies. The method allows to identify patterns of faults occurring in a cluster of bindings; in this way the energy consumption can be further optimized also through the building management staff by informing occupants of their energy usage and educating them to be proactive in their energy consumption. Finally, in the context of smart buildings, the common detected outliers in the cluster of buildings demonstrate that the management of a smart district can be operated with the whole buildings cluster approach

Pharmaceutical compositions comprising rifaximin, processes for their preparation and their use in the treatment of vaginal infections

The invention relates generally to pharmaceutical compositions comprising rifaximin effective at treating vaginal infections, and in particular bacterial vaginosis. The pharmaceutical compositions comprising rifaximin granules are characterized in that they release rifaximin in the vagina in a controlled way. The present invention also relates to processes for preparations of the rifaximin pharmaceutical compositions and their use in the treatment of vaginal infections. Effective dosages and courses of treatment useful and effective at recovering from the disease and preventing any possible relapse are also provided

Composizioni farmaceutiche comprendenti rifaximina, processi per la loro preparazione e loro uso nel trattamento di infezioni vaginali

L’oggetto dell’invenzione è costituito da composizioni a rilascio controllato, comprendenti granuli di rifaximina insieme con eccipienti farmaceuticamente accettabili, caratterizzate da rilasciare rifaximina in modo controllato nella vagina. Sono descritti anche processi per la loro preparazione e loro uso nel trattamento di infezioni vaginali, in particolare, vaginosi batteriche. Sono inoltre descritti i dosaggi di rifaximina utili ed efficaci per il raggiungimento della guarigione dalla malattia e prevenzione di recidive

Sensitivity Based Feature Selection for Recurrent Neural Network Applied to Forecasting of Heating Gas Consumption

The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead forecasting 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 earlier for the feed-forward network. The recurrent network can perform well even with 50% of features. This brings significant benefits in scenarios, where the neural network is used as a blackbox model of building consumption, which is called by an optimizer that minimizes the consumption. The reduction of input dimensionality leads to reduction of costs related to measurement equipment, but also costs related to data transfer

Indoor lighting fault detection and diagnosis using a data fusion approach

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 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 operatio