9678327ebb46bd6d2945fc8772bd943a energies_a2018v11n5iENG.pdf 680b1ce6feea9ca4888d9e2e19b2b7bd08d74bce energies_a2018v11n5iENG.pdf 36afa6e568fe481d22927621f046d549712f1cb0b20b9068171a431e3d09eee6 energies_a2018v11n5iENG.pdf Title: Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms Subject: We propose a simple tool to help the energy management of a large building stock defining clusters of buildings with the same function, setting alert thresholds for each cluster, and easily recognizing outliers. The objective is to enable a building management system to be used for detection of abnormal energy use. We start reviewing energy performance indicators, and how they feed into data visualization (DataViz) tools for a large building stock, especially for university campuses. After a brief presentation of the University of Turin's building stock which represents our case study, we perform an explorative analysis based on the Multidimensional Detective approach by Inselberg, using the Scatter Plot Matrix and the Parallel Coordinates methods. The k-means clustering algorithm is then applied on the same dataset to test the hypotheses made during the explorative analysis. Our results show that DataViz techniques provide quick and user-friendly solutions for the energy management of a large stock of buildings. In particular, they help identifying clusters of buildings and outliers and setting alert thresholds for various Energy Efficiency Indices. Keywords: Energy Efficiency Indices; data visualization; clustering algorithms; university campus; energy management Author: Dario Cottafava, Giulia Sonetti, Paolo Gambino and Andrea Tartaglino Creator: LaTeX with hyperref package Producer: pdfTeX-1.40.18 CreationDate: Mon May 21 13:18:46 2018 CEST ModDate: Tue Apr 29 16:57:30 2025 CEST Custom Metadata: yes Metadata Stream: yes Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 18 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 3517344 bytes Optimized: yes PDF version: 1.5 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- KHBKBS+URWPalladioL-Ital Type 1 Custom yes yes no 4130 0 GBPWAY+URWPalladioL-Bold Type 1 Custom yes yes no 4133 0 NVKPNY+URWPalladioL-Roma Type 1 Custom yes yes no 4136 0 OXBKYU+TeXGyreAdventor-Bold Type 1 Custom yes yes no 4140 0 QDIDFO+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi 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Builtin yes yes no 3829 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-05-01 02:10:02 CEST RepresentationInformation: energies_a2018v11n5iENG.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-04-30 07:56:49 CEST Size: 3517344 Format: PDF Version: 1.5 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf Profile: Linearized PDF PDFMetadata: Objects: 0 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: true CenterWindow: false DisplayDocTitle: false NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Children: Item: Title: Motivation and Problem Identification Item: Title: Current Paper Aim and Structure Item: Title: Large Scale Buildings Energy Monitoring Methods Children: Item: Title: Data Visualization Children: Item: Title: The Scatter Plot Matrix Item: Title: The Parallel Coordinates Item: Title: Data Clustering Algorithms Children: Item: Title: Distance Metrics Item: Title: Evaluation Item: Title: Clustering Algorithms Item: Title: Methodology Children: Item: Title: Dataset and Indices Description Item: Title: k-Means Algorithm Item: Title: Results Children: Item: Title: Cluster Identification Children: Item: Title: Cluster Hypothesis Item: Title: Data Visualization Techniques Item: Title: Clustering Algorithm Item: Title: Comparison between DataViz and k-Means Clusters Item: Title: Setting Thresholds Item: Title: Monitoring Trends Item: Title: Discussion Item: Title: Conclusions Item: Title: References Filters: FilterPipeline: FlateDecode Images: Image: NisoImageMetadata: FormatName: image/png ImageWidth: 640 ImageHeight: 480 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Fonts: Type1: Font: BaseFont: BFWAFQ+PazoMath-Italic FontSubset: true 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true EncodingDictionary: Differences: true Font: BaseFont: EZPDQL+CMEX10 FontSubset: true FirstChar: 0 LastChar: 105 FontDescriptor: FontName: EZPDQL+CMEX10 Flags: Symbolic FontBBox: -24, -2960, 1454, 772 FontFile: true Font: BaseFont: IKXQUG+PazoMath FontSubset: true FirstChar: 165 LastChar: 229 FontDescriptor: FontName: IKXQUG+PazoMath Flags: Symbolic FontBBox: -40, -283, 878, 946 FontFile: true XMP: application/pdf Dario Cottafava, Giulia Sonetti, Paolo Gambino and Andrea Tartaglino We propose a simple tool to help the energy management of a large building stock defining clusters of buildings with the same function, setting alert thresholds for each cluster, and easily recognizing outliers. The objective is to enable a building management system to be used for detection of abnormal energy use. We start reviewing energy performance indicators, and how they feed into data visualization (DataViz) tools for a large building stock, especially for university campuses. After a brief presentation of the University of Turin's building stock which represents our case study, we perform an explorative analysis based on the Multidimensional Detective approach by Inselberg, using the Scatter Plot Matrix and the Parallel Coordinates methods. The k-means clustering algorithm is then applied on the same dataset to test the hypotheses made during the explorative analysis. Our results show that DataViz techniques provide quick and user-friendly solutions for the energy management of a large stock of buildings. In particular, they help identifying clusters of buildings and outliers and setting alert thresholds for various Energy Efficiency Indices. Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms 2018-05-21T19:18:46+08:00 LaTeX with hyperref package 2025-04-29T16:57:30+02:00 2025-04-29T16:57:30+02:00 Energy Efficiency Indices; data visualization; clustering algorithms; university campus; energy management pdfTeX-1.40.18 False This is pdfTeX, Version 3.14159265-2.6-1.40.18 (TeX Live 2017/W32TeX) kpathsea version 6.2.3 uuid:d77fd314-81ae-4aa2-96ae-80f577b2bcbf uuid:f5b24ad0-f81e-4f90-a327-30ae1c6abd4b Pages: Page: Sequence: 1 Annotations: Annotation: Subtype: Link Rect: 75, 757, 202, 793 Annotation: Subtype: Link Rect: 474, 757, 519, 793 Annotation: Subtype: Link Rect: 160, 659, 173, 674 Annotation: Subtype: Link Rect: 247, 659, 260, 674 Annotation: Subtype: Link Rect: 341, 659, 354, 674 Annotation: Subtype: Link Rect: 467, 554, 519, 575 Annotation: Subtype: Link Rect: 203, 248, 210, 257 ActionDest: 1923 Annotation: Subtype: Link Rect: 188, 221, 195, 230 ActionDest: 1923 Annotation: 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