Data mining and machine learning in building energy analysis / Frédéric Magoulès, Hai-Xiang Zhao

Von:

Magoulès, Frédéric [VerfasserIn]

Mitwirkende(r):

Zhao, Hai-Xiang [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Computer engineering seriesVerlag: London ; Hoboken, NJ : ISTE : John Wiley & Sons, Inc, 2016Beschreibung: 1 online resource (xiv, 164 pages)ISBN:

9781118577592
9781118577486
1118577590
9781848214224
9781118577691
1118577698

Schlagwörter:

Andere physische Formen: 9781118577691 | 9781848214224 | Erscheint auch als: 9781118577691 Druck-Ausgabe | Erscheint auch als: 1118577698 Druck-Ausgabe | Erscheint auch als: 9781848214224 Druck-AusgabeDDC-Klassifikation:

006.312
006.3
696

LOC-Klassifikation:

QA76.9.D343

Online-Ressourcen:

Zugang im Netz des KIT

Zusammenfassung: Cover -- Title Page -- Copyright -- Contents -- Preface -- Introduction -- Chapter 1: Overview of Building Energy Analysis -- 1.1. Introduction -- 1.2. Physical models -- 1.3. Gray models -- 1.4. Statistical models -- 1.5. Artificial intelligence models -- 1.5.1. Neural networks -- 1.5.2. Support vector machines -- 1.6. Comparison of existing models -- 1.7. Concluding remarks -- Chapter 2: Data Acquisition for Building Energy Analysis -- 2.1. Introduction -- 2.2. Surveys or questionnaires -- 2.3. Measurements -- 2.4. Simulation -- 2.4.1. Simulation software -- 2.4.2. Simulation process -- 2.4.2.1. Simulation details -- 2.4.2.2. Simulation of one single building -- 2.4.2.3. Simulation of multiple buildings -- 2.5. Data uncertainty -- 2.6. Calibration -- 2.7. Concluding remarks -- Chapter 3: Artificial Intelligence Models -- 3.1. Introduction -- 3.2. Artificial neural networks -- 3.2.1. Single-layer perceptron -- 3.2.2. Feed forward neural network -- 3.2.3. Radial basis functions network -- 3.2.4. Recurrent neural network -- 3.2.5. Recursive deterministic perceptron -- 3.2.6. Applications of neural networks -- 3.3. Support vector machines -- 3.3.1. Support vector classification -- 3.3.2. ε-support vector regression -- 3.3.3. One-class support vector machines -- 3.3.4. Multiclass support vector machines -- 3.3.5. υ-support vector machines -- 3.3.6. Transductive support vector machines -- 3.3.7. Quadratic problem solvers -- 3.3.7.1. Interior point method -- 3.3.8. Applications of support vector machines -- 3.4. Concluding remarks -- Chapter 4: Artificial Intelligence for Building Energy Analysis -- 4.1. Introduction -- 4.2. Support vector machines for building energy prediction -- 4.2.1. Energy prediction definition -- 4.2.2. Practical issues -- 4.2.2.1. Operation flow -- 4.2.2.2. Experimental environment -- 4.2.2.3. Data preprocessing.PPN: PPN: 883632837Package identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PQE

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