TY - GEN
T1 - One-day-ahead hourly load forecasting of smart building using a hybrid approach
AU - Huang, Chao Ming
AU - Yang, Hong Tzer
AU - Huang, Yann Chang
AU - Huang, Kun Yuan
N1 - Funding Information:
The authors gratefully acknowledge the financial supports from the National Science Council, Taiwan, R.O.C. under Grant No. 102-3113-P-006-015.
Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - This paper proposes a hybrid approach to solve the one-day-ahead hourly load forecasting of smart building. The electricity consumption of a smart building is inherently nonlinear and dynamic and heavily dependent on the habitual nature of power demand, activities of daily living and on holidays or weekends, so it is often difficult to construct an adequate forecasting model for this type of load. To address this problem, this paper proposes a hybrid approach combining self-organizing map (SOM), learning vector quantization (LVQ), and fuzzy inference method to offer more adequate forecasting model for smart building. The proposed model comprises classification stage, forecasting stage, and correction stage. The forecasting results show that the proposed approach provides a robust and appropriate forecasting model.
AB - This paper proposes a hybrid approach to solve the one-day-ahead hourly load forecasting of smart building. The electricity consumption of a smart building is inherently nonlinear and dynamic and heavily dependent on the habitual nature of power demand, activities of daily living and on holidays or weekends, so it is often difficult to construct an adequate forecasting model for this type of load. To address this problem, this paper proposes a hybrid approach combining self-organizing map (SOM), learning vector quantization (LVQ), and fuzzy inference method to offer more adequate forecasting model for smart building. The proposed model comprises classification stage, forecasting stage, and correction stage. The forecasting results show that the proposed approach provides a robust and appropriate forecasting model.
UR - http://www.scopus.com/inward/record.url?scp=84928239081&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84928239081&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-04573-3_56
DO - 10.1007/978-3-319-04573-3_56
M3 - Conference contribution
AN - SCOPUS:84928239081
T3 - Lecture Notes in Electrical Engineering
SP - 453
EP - 460
BT - Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013
A2 - Chen, Cheng-Yi
A2 - Yang, Cheng-Fu
A2 - Juang, Jengnan
PB - Springer Verlag
T2 - 2nd International Conference on Intelligent Technologies and Engineering Systems, ICITES 2013
Y2 - 12 December 2013 through 14 December 2013
ER -
