Development of a Mobile Application for a Home Energy Cloud Monitoring System with Prediction Technology Using Artificial Intelligence

• Main Authors: meng-hsuan shen, 沈孟璇
• Other Authors: Jui-Sheng Chou
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/dsj44m

碩士 === 國立臺灣科技大學 === 營建工程系 === 107 === Because Taiwan contains a relatively small and narrow terrain with its four sides surrounding by the ocean, reducing the environmental burdens incurred by power generation is a continual endeavor of the government despite the stable supply of home energy. In October 2016, the Taiwanese government first implemented the Residential- and Commercial-Based Simplified Time-of-Use Program, which differs from previous programs for low-tension users. On April 1, 2018, the government increased the overall energy cost by 3%, in the hope that low-tension users can support household unit-based energy saving measures and reduce household energy consumption in Taiwan. Currently, most households in Taiwan install the conventional watt-hour meter that only displays the total energy consumption. If users do not install monitoring devices (such as smart meters) on their own, they cannot fully understand their energy consumption conditions at home or compare the differences in energy consumption at different periods. Under the energy saving subsidy policies implemented by the government, the number of users in Taiwan installing smart meters has progressively increased each year. In addition, the government increases the energy cost to discourage energy consumption. The Taiwan Power Company has also announced new rate schedules such as the Residential- and Commercial-Based Two-Stage and Three-Stage Time-of-Use rates, providing household users with a variety of energy plans from which they may choose. However, whether the new rates are more attractive to users is difficult to assess using the quantified data of bimonthly energy bills currently used by the Taiwan Power Company. According to the aforementioned assertions, this study was conducted to establish a convenient real-time energy monitoring system that can be integrated with various types of sensors for capturing energy data, the conventional watt-hour meter installed at home, or the electricity meter that can be installed by submitting a request form to the Taiwan Power Company. The energy data captured by the proposed system can be automatically sent to a preset online database, allowing the data to be uploaded to a cloud server and displayed on the user’s smart mobile device. In addition, if the system captures data on environmental factors (e.g., indoor and outdoor temperature and humidity) influential to energy consumption parameters, the artificial intelligence (AI) technology proposed in this study can be easily used to display predicted energy data on the user’s smart device. This enables users to monitor their home energy consumption and identify abnormal consumption in real time. Through a cloud server, the proposed system displays real-time home energy data on users’ mobile devices. In addition, AI languages were coded to apply decision tree analysis to easily predicting the energy consumption of household units. The prototype system can assist users in visualizing their energy consumption with respect to time and thereby understanding their consumption patterns. The predicted energy data also allow the user to estimate their energy costs and serve as a reference for changing their current time-of-use program. Keywords: household energy consumption, cloud monitoring, artificial intelligence prediction, mobile application