TY - GEN
T1 - Load scheduling based on an advanced real-time price forecasting model
AU - Luo, Xing
AU - Zhu, Xu
AU - Lim, Eng Gee
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - The real-time price (RTP) of electricity becomes a trend in next decades since it is able to moderate power consumption of customers in rush hours. In order to save bills, the residential customers get involved in load shifting and the service time of domestic electric appliances are scheduled more intelligently. In this paper, first of all, according to the historical prices information, an advanced RTP forecasting model is proposed on the basis of the least-square (LS) fitting function and the grey prediction technique (GPT). Secondly, considering the factors such as predicted real-time price, type of appliances, user's preferences behaviors etc., a load scheduling approach is introduced to schedule the operating time of home appliances intelligently and estimate the resulting electricity bill. Simulation results verify the effectiveness of the proposed RTP forecasting model. The results also show that the load scheduling approach is able to accomplish peak load shifting and reduce the bill by around 12% in a typical day.
AB - The real-time price (RTP) of electricity becomes a trend in next decades since it is able to moderate power consumption of customers in rush hours. In order to save bills, the residential customers get involved in load shifting and the service time of domestic electric appliances are scheduled more intelligently. In this paper, first of all, according to the historical prices information, an advanced RTP forecasting model is proposed on the basis of the least-square (LS) fitting function and the grey prediction technique (GPT). Secondly, considering the factors such as predicted real-time price, type of appliances, user's preferences behaviors etc., a load scheduling approach is introduced to schedule the operating time of home appliances intelligently and estimate the resulting electricity bill. Simulation results verify the effectiveness of the proposed RTP forecasting model. The results also show that the load scheduling approach is able to accomplish peak load shifting and reduce the bill by around 12% in a typical day.
UR - http://www.scopus.com/inward/record.url?scp=84964266932&partnerID=8YFLogxK
U2 - 10.1109/CIT/IUCC/DASC/PICOM.2015.186
DO - 10.1109/CIT/IUCC/DASC/PICOM.2015.186
M3 - Conference Proceeding
AN - SCOPUS:84964266932
T3 - Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
SP - 1252
EP - 1257
BT - Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
A2 - Atzori, Luigi
A2 - Jin, Xiaolong
A2 - Jarvis, Stephen
A2 - Liu, Lei
A2 - Calvo, Ramon Aguero
A2 - Hu, Jia
A2 - Min, Geyong
A2 - Georgalas, Nektarios
A2 - Wu, Yulei
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Secure Computing, DASC 2015 and 13th IEEE International Conference on Pervasive Intelligence and Computing, PICom 2015
Y2 - 26 October 2015 through 28 October 2015
ER -