Data mining and machine learning in building energy analysis

Data mining and machine learning in building energy analysis

Detalles Bibliográficos
Otros Autores: Magoulès, F. author (author), Zhao, Hai-Xiang, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England ; Hoboken, New Jersey : ISTE 2016.
Edición:1st ed
Colección:Computer engineering series.
Materias:
Buildings > Energy conservation > Research.
Data mining.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849116206719
Tabla de Contenidos:
  • 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.
  • 4.2.2.4. Model selection
  • 4.2.3. Support vector machines for prediction
  • 4.2.3.1. Prediction of single building energy
  • 4.2.3.2. Extensive model evaluation
  • 4.2.3.3. Prediction of multiple buildings energy
  • 4.3. Neural networks for fault detection and diagnosis
  • 4.3.1. Description of faults
  • 4.3.2. RDP in fault detection
  • 4.3.2.1. Introduce faults to the simulated building
  • 4.3.2.2. Experiments and results
  • 4.3.3. RDP in fault diagnosis
  • 4.4. Concluding remarks
  • Chapter 5: Model Reduction for Support Vector Machines
  • 5.1. Introduction
  • 5.2. Overview of model reduction
  • 5.2.1. Wrapper methods
  • 5.2.2. Filter methods
  • 5.2.3. Embedded methods
  • 5.3. Model reduction for energy consumption
  • 5.3.1. Introduction
  • 5.3.2. Algorithm
  • 5.3.3. Feature set description
  • 5.4. Model reduction for single building energy
  • 5.4.1. Feature set selection
  • 5.4.2. Evaluation in experiments
  • 5.5. Model reduction for multiple buildings energy
  • 5.6. Concluding remarks
  • Chapter 6: Parallel Computing for Support Vector Machines
  • 6.1. Introduction
  • 6.2. Overview of parallel support vector machines
  • 6.3. Parallel quadratic problem solver
  • 6.4. MPI-based parallel support vector machines
  • 6.4.1. Message passing interface programming model
  • 6.4.2. Pisvm
  • 6.4.3. Psvm
  • 6.5. MapReduce-based parallel support vector machines
  • 6.5.1. MapReduce programming model
  • 6.5.2. Caching technique
  • 6.5.3. Sparse data representation
  • 6.5.4. Comparison of MRPsvm with Pisvm
  • 6.6. MapReduce-based parallel ε-support vector regression
  • 6.6.1. Implementation aspects
  • 6.6.2. Energy consumption datasets
  • 6.6.3. Evaluation for building energy prediction
  • 6.7. Concluding remarks
  • Summary and Future of Building Energy Analysis
  • Building energy consumption
  • Predicting building energy consumption.
  • Detection and diagnosis of building energy faults
  • Feature selection and model reduction
  • Parallel computing
  • Future work
  • Bibliography
  • Index.

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