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.