Data Mining and Machine Learning in Building Energy Analysis

Inbunden, Engelska, 2016

Av Frédéric Magoules, Hai-Xiang Zhao, France) Magoules, Frederic (Ecole Centrale Paris, France) Zhao, Hai-Xiang (Amadeus

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The energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.

Produktinformation

Utgivningsdatum2016-01-08
Mått163 x 241 x 15 mm
Vikt431 g
FormatInbunden
SpråkEngelska
Antal sidor186
FörlagISTE Ltd and John Wiley & Sons Inc
ISBN9781848214224

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Preface ixIntroduction xiChapter 1. Overview of Building Energy Analysis 11.1. Introduction 11.2. Physical models 31.3. Gray models 61.4. Statistical models 61.5. Artificial intelligence models 81.5.1. Neural networks 81.5.2. Support vector machines 131.6. Comparison of existing models 141.7. Concluding remarks . 16Chapter 2. Data Acquisition for Building Energy Analysis 172.1. Introduction 172.2. Surveys or questionnaires 182.3. Measurements 212.4. Simulation 252.4.1. Simulation software 262.4.2. Simulation process 282.5. Data uncertainty 342.6. Calibration 352.7. Concluding remarks 37Chapter 3. Artificial Intelligence Models 393.1. Introduction 393.2. Artificial neural networks 403.2.1. Single-layer perceptron 413.2.2. Feed forward neural network 433.2.3. Radial basis functions network 443.2.4. Recurrent neural network 473.2.5. Recursive deterministic perceptron 493.2.6. Applications of neural networks 513.3. Support vector machines 533.3.1. Support vector classification 543.3.2. ε-support vector regression 593.3.3. One-class support vector machines 623.3.4. Multiclass support vector machines 633.3.5. v-support vector machines 643.3.6. Transductive support vector machines 653.3.7. Quadratic problem solvers . 673.3.8. Applications of support vector machines 753.4. Concluding remarks 76Chapter 4. Artificial Intelligence for Building Energy Analysis 794.1. Introduction 794.2. Support vector machines for building energy prediction 804.2.1. Energy prediction definition 804.2.2. Practical issues 814.2.3. Support vector machines for prediction 854.3. Neural networks for fault detection and diagnosis 914.3.1. Description of faults 944.3.2. RDP in fault detection 954.3.3. RDP in fault diagnosis 1004.4. Concluding remarks 102Chapter 5. Model Reduction for Support Vector Machines 1035.1. Introduction 1035.2. Overview of model reduction 1045.2.1. Wrapper methods 1055.2.2. Filter methods 1065.2.3. Embedded methods 1075.3. Model reduction for energy consumption 1085.3.1. Introduction 1085.3.2. Algorithm 1095.3.3. Feature set description 1115.4. Model reduction for single building energy 1125.4.1. Feature set selection 1125.4.2. Evaluation in experiments 1145.5. Model reduction for multiple buildings energy 1165.6. Concluding remarks 119Chapter 6. Parallel Computing for Support Vector Machines 1216.1. Introduction 1216.2. Overview of parallel support vector machines 1226.3. Parallel quadratic problem solver 1236.4. MPI-based parallel support vector machines 1276.4.1. Message passing interface programming model 1276.4.2. Pisvm 1296.4.3. Psvm 1306.5. MapReduce-based parallel support vector machines 1306.5.1. MapReduce programming model 1316.5.2. Caching technique 1336.5.3. Sparse data representation 1336.5.4. Comparison of MRPsvm with Pisvm 1346.6. MapReduce-based parallel ε-support vector regression 1386.6.1. Implementation aspects 1386.6.2. Energy consumption datasets 1396.6.3. Evaluation for building energy prediction 1406.7. Concluding remarks 142Summary and Future of Building Energy Analysis 145Bibliography 149Index 163