An Air-conditioning Load Forecasting Based on Dynamical Combined Re- sidual Error Correction

Accurate air-conditioning load forecasting is the precondition for the optimal control and energy saving operation of central air-conditioning system. However, the single forecasting method, such as autoregressive integrated moving average (ARIMA), grey model (GM), multiple linear regression (MLR) and artificial neural network (ANN), has not enough accuracy. In order to improve the accuracy of air-conditioning load forecasting, the combination forecast develops. But so far there are no literatures that explain how to choose the single forecasting methods to build the combination forecast that can further improve the forecasting accuracy. To further improve the forecasting accuracy, a forecasting method with dynamical combined residual error correction is proposed. The residual error correction model and its combination ways are analyzed, and the very high accuracy with mathematical proof is realized in this paper. A case study indicates that the dynamical combination ways proposed in this paper can further improve the accuracy of combination forecasting and satisfy the accuracy requirement of air-conditioning load forecasting.