基于神经网络的热电联产机组热负荷和电负荷预测

Prediction of heat and electric load of cogeneration unit based on neural network

针对热电联产机组每年发电计划制定及调整期间,地方电厂以满足供热为由频繁要求调整计划的现象,本文提出一种以热定电的电负荷预测方法。该方法基于地方热负荷,对要求调整发电计划的电厂进行热负荷假设检验提取,并用Elman神经网络方法预测热负荷,最后采用灰色神经网络方法来确定电厂发电量。该方法对于确定热电联产机组发电计划、控制购电成本具有重要的意义。

During the formulation and adjustment of the annual power generation plan of cogeneration units, the local power plants frequently require adjusting the plan to meet the heating requirements. To solve this problem, this paper proposes a method for predicting the electric load. On the basis of the local thermal load, this method uses hypothesis test method to extract the thermal load of the power plant that needs to regulate the power generation plan, and forecast the thermal load by the improved Elman neural network method, then determines the generating capacity of the power plant via the grey neural network method. This method is of vital significance to reasonably determine the power generation plan and control the purchase cost according to the heating demand put forward by the power plant.