Version 1
: Received: 20 April 2021 / Approved: 23 April 2021 / Online: 23 April 2021 (09:46:32 CEST)
How to cite:
Cho, M.; Li, C. The Establishment and Prediction of Regression Models of Energy Sales and System Peak Loading By Considering AMI Data for High Voltage Customers. Preprints2021, 2021040622. https://doi.org/10.20944/preprints202104.0622.v1
Cho, M.; Li, C. The Establishment and Prediction of Regression Models of Energy Sales and System Peak Loading By Considering AMI Data for High Voltage Customers. Preprints 2021, 2021040622. https://doi.org/10.20944/preprints202104.0622.v1
Cho, M.; Li, C. The Establishment and Prediction of Regression Models of Energy Sales and System Peak Loading By Considering AMI Data for High Voltage Customers. Preprints2021, 2021040622. https://doi.org/10.20944/preprints202104.0622.v1
APA Style
Cho, M., & Li, C. (2021). The Establishment and Prediction of Regression Models of Energy Sales and System Peak Loading By Considering AMI Data for High Voltage Customers. Preprints. https://doi.org/10.20944/preprints202104.0622.v1
Chicago/Turabian Style
Cho, M. and Chien-hsing Li. 2021 "The Establishment and Prediction of Regression Models of Energy Sales and System Peak Loading By Considering AMI Data for High Voltage Customers" Preprints. https://doi.org/10.20944/preprints202104.0622.v1
Abstract
This paper uses the complex regression analysis method to establish the 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 paper used 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 were adopted to forecast summer peak load. 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 although its forecasting model accuracy is low only when mixed with temperature and high voltage demands. So, when mixed with high voltage demand data and system generating capacity for 24 hours to forecast peak load, the average error is ± 0.87% and in the majority of its energy sales forecasting model of average error is ±3%. This result can provide power company as future reference.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
