Fault detection analysis using data mining techniques for a cluster of smart office buildings
Section snippets
Introduction and background knowledge
Buildings are becoming more and more complex energy systems consisting of several elements i.e. heating/cooling systems, ventilation systems, lighting and control systems etc. In addition, buildings have multifarious activities and the occupants may have different demands from a building. Even though building ramification is growing, communication between the participants and the building elements during the building life is poor (Djuric & Novakovic, 2009). The building energy system and the
Buildings cluster and data description
The cluster of buildings is located within the Italian National Agency for New technologies, Energy and Sustainable Economic Development (ENEA) Casaccia research center and includes buildings F66–F67–F68–F69–F70–F71–F72–F73, positioned in two different blocks. A first block, consisting of three contiguous buildings is oriented along the axis NW–SE, while the second block consists of 5 buildings and its main orientation is NE–SW as shown in Fig. 1. The eight buildings have similar
Proposed methods
One of the effective ways of analyzing large data is to identify recurring patterns in the raw data. Clustering and classification are two common techniques used for finding hidden patterns in data sets. Discovering the patterns in data before applying the outliers detection algorithm is very useful to find anomalies in the building energy consumption. Outliers are cases that have data values very different from the data values for the majority of cases in the data set. Statistical-based (
Methodology
The proposed approach was applied to each building of the cluster for both winter and summer data. This section contains the application of each method described in Section 3.
For all simulations performed in this work, the values of the active electrical power for lighting and the total active electrical power of each building are considered as dependent variables. The independent variables considered are: date, day of the week, time of the day, average indoor temperature, average outdoor
Results and discussion
The results obtained from the analysis of fault detection conducted separately for each building, for the two dependent variables, and for the two time periods are summarized and presented in this section.
Conclusions
The research was aimed at testing the potential of using data mining techniques and ANN BEM in combination with outliers detection for an automated fault detection process. The application of the proposed approach can improve fault detection process in building application by reducing the number of false anomalies.
The methods proposed and implemented have proven adequate for detection purposes with different potentials and limitations. In particular the neural ensemble method has always proven
Glossary
- ANN
- artificial neural network
- ANNE
- artificial neural networks ensemble
- BEM
- Basic Ensemble Method
- BEMS
- building energy management system
- CART
- classification and regression tree
- DBSCAN
- Density-Based Spatial Clustering of Applications with Noise
- FDD
- fault detection and diagnosis
- GESD
- generalized extreme studentized deviate
- h
- user-specified constant in peak detection method
- HVAC
- heating, ventilation and air conditioning
- k
- number of right/left neighbors of xi
- mean of all positive values of the peak function
- minPts
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2023, Renewable and Sustainable Energy ReviewsAn outlier management framework for building performance data and its application to the power consumption data of building energy systems in non-residential buildings
2023, Journal of Building EngineeringCitation Excerpt :The lack of statistical supports would limit their further application to data with larger scale. The building performance data is valuable for various research interests, such as data-driven fault detection, diagnosis, and estimation (FDD&E) [7,17–27], performance optimization, predictive modeling, building power consumption pattern recognition [8,13–16,23,28–33], etc. This section will focus to review the outlier management progress in the building power data.