高校采暖负荷短期预测研究

Research for Short-term Prediction of the Heating Load in a University

高校采暖建筑种类繁杂,缺乏有效的短期采暖负荷预测手段,造成了能源浪费。以某高校2015—2016年采暖历史数据为基础,以最高温度和最低温度为主要影响因素,建立了2-10-1结构的BP神经网络模型。结果表明:BP神经网络模型训练、验证及测试精度分别为0.048、0.054和0.096,总关联系数为0.975 5,可用于高校采暖负荷短期预测,为解决能源供需不平衡问题提供了科学手段。

The types of buildings which need the heating supply are complex in universities, and there is no effective method for forecasting the heating load in a short term. All these result in a waste of energy. In this paper, the BP neural network model was established in a 2-10-1 structure based on historical data of 2015-2016 heating load in a university which is located in cold regions. The main influential factors were the highest and lowest temperature. The results showed that the training,verifying and testing accuracy of BP neural network were respectively 0.048, 0.054 and 0.096, and R was 0.975 5. The method could be used for forecasting short-term load in college. At the same time it could also provide a scientific mean to solve the problem of the imbalance between energy supply and demand.