Alfonso Capozzoli

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

An energy fault detection analysis was performed for a cluster of buildings.Pattern recognition techniques coupled with outliers detection methods were used.Anomalies are detected during early morning, lunch break, and end of working hours.The methodology can be easily implemented in BEMS. 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.

Vacuum Insulation Panels: Analysis of the Thermal Performance of Both Single Panel and Multilayer Boards

The requirements for improvement in the energy efficiency of buildings, mandatory in many EU countries, entail a high level of thermal insulation of the building envelope. In recent years, super-insulation materials with very low thermal conductivity have been developed. These materials provide satisfactory thermal insulation, but allow the total thickness of the envelope components to be kept below a certain thickness. Nevertheless, in order to penetrate the building construction market, some barriers have to be overcome. One of the main issues is that testing procedures and useful data that are able to give a reliable picture of their performance when applied to real buildings have to be provided. Vacuum Insulation Panels (VIPs) are one of the most promising high performing technologies. The overall, effective, performance of a panel under actual working conditions is influenced by thermal bridging, due to the edge of the panel envelope and to the type of joint. In this paper, a study on the critical issues related to the laboratory measurement of the equivalent thermal conductivity of VIPs and their performance degradation due to vacuum loss has been carried out utilizing guarded heat flux meter apparatus. A numerical analysis has also been developed to study thermal bridging effect when VIP panels are adopted to create multilayer boards for building applications.

Review on Performance Metrics for Energy Efficiency in Data Center: The Role of Thermal Management

Energy consumption and thermal performance are the two most important tasks in data centers (DCs) facility management. In recent years, to monitor and control their variation several performance metrics were introduced. In this paper an overview on the main important energy and thermal metrics is provided. A critical analysis to investigate mutual relations among metrics was performed, with the aim to clarify some physical aspects regarding the assessment of DC global energy performance.

Measuring energy efficiency in data centers

Energy efficiency in Data Centers (DCs) is currently becoming a topic of increasing importance, considering the rising prices of energy and the expansion of large data sets (Big Data) processing demand. A structured measurement framework that can be used to quantify energy efficiency is required to understand the opportunities for improving energy efficiency in DCs. In other words, a detailed analysis of energy metrics is needed. However, only a small step forward has been made in the measurement of DCs’ energy efficiency in recent years. Therefore, the measurement of energy efficiency in DCs, through a set of globally accepted metrics, is an ongoing challenge. This chapter presents a comprehensive overview of the existing energy, thermal and productivity metrics for DCs and a critical analysis that investigates the intertwined nature of their action areas. The study provides a general methodology that can be used to measure the energy efficiency of DCs through a holistic approach in which the advantages and the disadvantages of existing and emerging metrics are considered critically.

Temperature Field Real-Time Diagnosis by Means of Infrared Imaging in Data Elaboration Center

Data Elaboration Centers are characterized by high specific energy consumptions, due to the requirements for the working environment of IT machines and their large working loads. This is due to the fact that the control of the indoor air parameters is of paramount importance in order to maintain the servers in safe working conditions. Reliable diagnostic procedures are therefore needed. The work herewith presented proposes an innovative diagnostic method, which makes use of infrared thermal imaging for real-time monitoring of the air temperature field in the proximity to the server air inlet. The method is validated by means of an experimental assessment, with the aim to assess the capability of an industrial infrared camera to evaluate the air temperature field next to the inlet surface of the server racks, in real working conditions. Potential and limitations of this procedure are presented and discussed.

Building energy management through fault detection analysis using pattern recognition techniques applied on residual neural networks

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 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.