Prediction for Daily Loads of Air-Conditioning System

Abstract

Conventional commercial building air-conditioning systems typically employ heat storage systems that have sufficient heat source capacity to achieve the utilization of recovered waste heat and night electric power, and to meet peak instantaneous loads. In light of the increasing popularity of these systems, a need exists for the prediction of the daily loads of air-conditioning system took place in a systematic fashion. This work is concerned with a prediction problem for the daily loads by taking account the Autoregressive Integrated Moving Average (ARIMA) model. Some of technical considerations are examined. At the end of each day (at 22:00), the modelling is first done for a set of observed data by taking into account the ARIMA model. In this modelling procedure, the historical load profile data are directly used but the ambient temperature profile data are not used. Next, this ARIMA model obtained above is used to predict the daily load for the next day. The load profiles are updated every day on the basis of the newly obtained load data. The differences between predicted and actual loads are examined for days 266 in 1987. There is generally good agreement between predicted and actual loads. The strategy algorithms are now executed to update the nominal operation of the heat storage system to meet the predicted load for the next day.