The Establishment and Prediction of Energy Sales Regression Model in Considering with Prosperity and Temperature for High Voltage Customer

• Main Authors: WU,YI-HANG, 吳易翰
• Other Authors: CHO,MING-YUAN
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/mswppk

碩士 === 國立高雄應用科技大學 === 電機工程系博碩士班 === 104 === This thesis uses complex regression analysis method to establish customer’s load regression models, which consider economic indicators, temperature and rainfall. Furthermore, the proposed models are used to study the forecasting feasibility of the future energy sales and summer peak load demand. At first, this thesis uses least-squares techniques to derive regression models for considering economic indicators and temperature of 34 customer energy sales and total energy sales. Besides, the AMI high voltage customer demand data and system generating capacity for 24 hours are adopted to forecast summer peak load. Finally, the temperature sensitivity analysis is carried out to verify the result of service sector customers, energy intenstive customer and all high voltage customer energy sales. The above-mentioned data analysis tool is used by EViews software to achieve, in order to verify the feasibility of the research framework. The study found that through its energy intenstive customer and all high voltage customer aren’t sensitive to temperature, which shows forecasting model accuracy is low of only mixing with temperature and high voltage demand. So, mixing with high voltage demand data and system generating capacity for 24 hours to forecast peak load, which average error is ±0.87%. In the majority of its energy sales forecasting model of average error is ±3%. This result can be provided to power company as future reference.