标题：Public Buildings Baseline Load Forecasting Model
作者：Ma, Qing; Li, Qi-qiang
作者机构：[Ma, Qing; Li, Qi-qiang] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China.
会议名称：7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011)
会议日期：NOV 04-06, 2011
来源：MEMS, NANO AND SMART SYSTEMS, PTS 1-6
关键词：Baseline load; forecasting; neural networks; adjustment factor; proxy; event day
摘要：Based on the fact that public buildings baseline load is hard to predict effectively, a kind of BP neural networks forecasting model based on FCM optimization preprocesses which combines with adjustment factor is proposed. The method which adopts method of the FCM arithmetic divides the complicated historical data into gather of multiple proxy event day populations. Then, based on BP neural network forecasting model regulated by adjustment factor, public buildings baseline load forecasting model is introduced. The prediction results show that the prediction precision of the model is higher than that of linearity model, and it can predict the public buildings baseline load effectively.