A Study on Domestic Water Demand Prediction

• Main Authors: Chih-Hsien Cheng, 鄭志賢
• Other Authors: Tien-Chin Wang
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/73008774254015920073

碩士 === 義守大學 === 資訊管理學系碩士班 === 96 === Today, enterprises are forced to confront the challenges of global competition. They have had to grasp, and promptly make use of information ever since the Internet triggered more intensive competition in terms of time and space. Correctly anticipating client needs and quickly adapting to supply-demand trends are the foundation for enterprise''s sustainable development in the age of information explosion. In view of the nation’s self-awareness concerning raising economic, competitive advantages and the necessity of satisfying water resources demand, this research aims at constructing a prediction model of water demand and introducing: a water utilization pattern, a water consumption habit and an economic structure covering the next few years, in order to estimate water demand for water companies reference, and to benefit the nation, enterprises and people by managing water consumption effectively, resolving water shortage problems, and reducing the risk of domestic water use limitations or shortages. Although there are numerous models of water supply-demand, most researches have focused on supply-demand of annual water consumption. The Grey Prediction Model, of Grey''s theory, was used to predict the monthly water consumption in 12 branches of the Water Corporation, unlike the conventional prediction based on annual water consumption. The “uncertainty with little data” of Grey''s theory truly has more advantages, in terms of prediction, since the factors influencing water consumption were diversified and long-term historical statistics could not reflect current water consumption. The empirical results of the water consumption prediction model showed that according to average accuracy rate, the accuracy rate in using monthly water consumption data was above 95.7635% and the accuracy rate of five points rolling Grey Prediction Model reached up to 96.4274%, with more stable prediction results.