Prediction of Home Energy Consumption based on Appliance State Transition Models

• Main Authors: Chen, Yung-Chi, 陳勇旗
• Other Authors: 曹孝櫟
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/7b2zya

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 106 === Nowadays, more and more people concern the energy and environmental issues and would like to manage the use of electric power from a small-scale area such as a house, a building, or community. One of the most important tasks is to forecast the power consumption in next several minutes and/or hours so that people may cooperatively use their appliances in an asynchronous manner to alleviate the peak power consumption of an area. Different from conventional large-scale power consumption forecast schemes which are mainly based on artificial intelligence methods such as artificial neural network, this paper proposes a new approach to predict the energy consumption of a house and building based on appliance state transition models which can be gathered from a nonintrusive load monitoring (NILM) meter. First, the appliance usage patterns of a house or a building are obtained from the NILM meter. Then, appliance state transition models can be established and they can be used to predict the energy consumption of a house or building efficiently. Simulation results indicate that only 3% to 5% prediction error is introduced for a typical house environment.