| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 104 === Building energy consumption becomes important in the age of high electricity bills. However, people cannot understand detail information about their energy consumption by reading their monthly bill. Energy consumption of a building depends on many factors such as building structure, human activities and weather. It is hard to understand the relation between these building factors and energy consumption, and also hard to gather these data. In this paper, we purpose a building expert system that can help user realize their houses in an easier and systematic way. First, we apply sensors on the smart phone and open data API to help the building factor collection so that we can finish most collection works by just one phone rather than many tools. Second, we combine reduced building factor set from EnergyPlus, which is an engineering building modeling tool, and support vector regression as our building energy consumption model method. The reduced factor set can achieve about 70% accuracy of the prediction of energy consumption [2] . Support vector regression is a statistical model that has a data-training step to make the model more fitting the dataset. We use the integrated model to predict an energy consumption and benchmark the real energy consumption. Third, we find that in different environment, the importance of the building factor will be different. Therefore, we apply sensitivity analysis on the historical data in order to suggest different building factor when user in the factor collection step. This helps user focus on the critical building factor and help accurate the prediction of energy consumption. Finally, we collect all users’ data including the values of all building factors and real energy consumption. We can have more information and based dataset for next user. The experiment shows app tool can achieve about 90% measurement accuracy and building model can achieve up to about 80% prediction accuracy.
|
|---|
