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

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.