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Short-term Electricity Forecasting of Air-conditioners of Hospital Using Artificial Neural Networks

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3 Author(s)
Chao-Rong Chen ; Dept. of Electr. Eng., Nat. Taipei Univ. of Technol. ; Shun-Chung Shih ; Shih-Cheng Hu

This paper proposes practical predictions of hospital air-conditioner electricity using the artificial neural network, owing to its excellent predict ability. The influence variables of hospital air-conditioner electricity are included temperature, relative humidity, the previous one hour electricity, the time in day, and some uncontrolled variables, e.g. the number of surgical operations, the number of persons; and some fix variables, e.g., the area of indoors, the number of sickbeds, etc. Therefore, the short-term forecasting of air conditioner electricity is difficult. But it is important to do energy management. There are three types of artificial neural network models to do the prediction of the next hour electricity load in the hospital, they are each day, weekday with outpatient considering, and weekday without outpatient considering. The results show that the predictions are excellent and able to assist the operators to do energy management

Published in:

Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES

Date of Conference:

2005