天津市办公建筑供暖负荷中短期预测

Short and medium-term prediction of heating load for office buildings in Tianjin

分析了日平均干球温度、节假日效应等因素与日平均负荷的关系,利用带残差修正的多元非线性回归对日平均负荷进行了预测。选用时段及日类型作为"相似日"判别要素,根据相应的相似日热负荷分布系数对逐时热负荷进行了预测。实际的案例分析表明,这种负荷预测模型对办公建筑的热负荷有较好的预测效果。

Analyses the relationship between the average daily dry-bulb temperature and holiday effect and the daily average load, and establishes the multivariate nonlinear regression model with residual error correction to predict the daily average load. Selects date and day type as the discriminatory factors of ＂similar day＂, and predicts the hourly heating load according to load distribution coefficient of the similar day. Case studies show that the prediction model has high prediction accuracy for the office buildings.