NDLI: A Novel Air-Conditioning Load Prediction Based on ARIMA and BPNN Model

Content Provider	IEEE Xplore Digital Library
Author	Li Xuemei Ding Lixing Shao Ming Xu Gang Li Jibin
Copyright Year	2009
Abstract	Accurate air-conditioning load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Many forecasting techniques such as support vector machine (SVM), artificial neural network (ANN), autoregressive integrated moving average (ARIMA) and grey model, have been proposed in the field of air-conditioning load prediction. However, none of them has enough accuracy to satisfy the practical demand. Therefore, a novel method integrating ARIMA and Artificial Neural Network (ANN) is presented to forecast an air-conditioning load. ARIMA is suitable for linear prediction and ANN is suitable for nonlinear prediction. This paper also investigates the issue on how to effectively model short term air conditioning load time series with a new algorithm, which estimates the weights of the ANN and the parameters of ARMA model. Experimental results demonstrate that the hybrid air conditioning load forecasting model can be an effective way to improve forecasting accuracy achieved by either of the models used separately.
Starting Page	51
Ending Page	54
File Size	439747
Page Count	4
File Format	PDF
ISBN	9780769536996
DOI	10.1109/APCIP.2009.21
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Air conditioning ANN Demand forecasting Artificial neural networks Predictive models Automotive engineering Support vector machines hybrid model Load forecasting Optimal control ARIMA air-conditioning load forecasting Power engineering and energy Load modeling
Content Type	Text
Resource Type	Article

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