Data Mining and Machine Learning in Building Energy Analysis

Preface

The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, occupants and their behaviors, operation of sublevel components like heating, ventilation and air-conditioning systems. These complex properties make the prediction, analysis or fault detection/diagnosis of building energy consumption very difficult to perform accurately.

This book focuses on up-to-date data mining and machine-learning methods to solve these problems. This book first presents a review of recently developed models for solving prediction, analysis or fault detection/diagnosis of building energy consumption, including detailed and simplified engineering methods, statistical methods and artificial intelligence methods. Then, the methodology to simulate energy consumption profiles for single and multiple buildings is presented. Based on these datasets, support vector machine (SVM) models are trained and tested to do the prediction. The results from extensive experiments demonstrate high-prediction accuracy and robustness of these models. A recursive deterministic perceptron (RDP) neural network model is then used to detect and diagnose faulty building energy consumption. In the experiment, the RDP model shows a very high-detection ability. A new approach, based on the evaluation of RDP models, is also proposed here to diagnose faults. Since the selection of subsets of features significantly influences the performance ...