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Record Nr. |
UNINA9910767537603321 |
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Titolo |
Data-driven analytics for sustainable buildings and cities : from theory to application / / Xingxing Zhang, editor |
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Pubbl/distr/stampa |
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Singapore : , : Springer, , [2021] |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (446 pages) |
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Collana |
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Sustainable Development Goals Series |
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Disciplina |
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Soggetti |
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Sustainable buildings - Data processing |
Sustainable buildings - Statistical methods |
Smart cities |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Contents -- 1 The Evolving of Data-Driven Analytics for Buildings and Cities Towards Sustainability -- Abstract -- 1.1 Introduction -- 1.1.1 Background -- 1.1.2 Data-Driven Analytics for Sustainability Goals -- 1.2 Aim and Objectives -- 1.3 Motivations and Novelties -- 1.4 Structure and Contents -- References -- Energy in Buildings -- 2 Data-Driven Approaches for Prediction and Classification of Building Energy Consumption -- Abstract -- 2.1 Introduction -- 2.1.1 The Need for Energy Consumption Analysis -- 2.1.2 Advantage and Motivation -- 2.1.3 Usage of Building Energy and Performance Data -- 2.1.4 Proposed Methodologies for Building Energy Consumption -- 2.1.5 Data-Driven Approaches -- 2.1.6 Data-Driven Prediction Models -- 2.1.6.1 Artificial Neural Networks -- 2.1.6.2 Support Vector Machine -- 2.1.6.3 Statistical Regression -- 2.1.6.4 Decision Tree -- 2.1.6.5 Genetic Algorithms -- 2.1.7 Data-Driven Classification Approaches -- 2.1.7.1 K-Means Cluster -- 2.1.7.2 Self-organizing Map -- 2.1.7.3 Hierarchical Clustering -- 2.2 Practical Application of Data-Driven Approaches -- 2.2.1 R & -- D Works and Practical Applications -- 2.2.1.1 Prediction -- Prediction Application of ANNs -- Prediction Application of SVM -- Prediction Application of Statistical Regression -- Prediction Application of Decision Tree -- Prediction Application of Genetic Algorithms -- |
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