基于气温与日期类型的改进BP网络热负荷预测

Improved BP Neural Network of Heat Load Forecasting Based on Temperature and Date Type

热负荷预测为城市集中供热系统提供数据支持,是实现按需供热的基础。热负荷的变化受外界各项因素特别是室外温度影响较大,为在满足供热系统需求量的同时做到节能与兼顾人体舒适度,提出基于气温与日期类型的热负荷预测方法。该方法将气温与日期类型进行量化并利用BP神经网络建立供热系统的热负荷预测模型。为保证预测精度采用遗传算法对神经网络连接权值和阈值进行优化,得到未来24小时的热负荷预测值。预测结果表明,此方法可以较准确地预测未来的热负荷,并达到按需供热和节能环保的目的。

The heat load forecasting provides data support for urban district heating systems, which is the basis of need-based heating. The change of heat load is greatly influenced by various exterior factors, especially the outdoor temperature. To meet demand of heating system, save energy and balance the comfort of human body, a kind of improved BP neural network method is proposed by temperature and date type. The temperature and date type are quantified and the heat load forecasting model is established by using BP neural network. To guarantee prediction accuracy, the genetic algorithm is used to optimize the weights and thresholds of the neural network, and from which the predicted value of the heat load in the next 24 hours is obtained. The predicted results from the model show that the proposed method can accurately predict the future heat load; and the goals of on-demand heating, energy conservation and environmental protection are achieved.