改进的深度回声网络在空调负荷预测中的应用

Application of an improved deep echo networkin air conditioning load forecasting

针对深度回声状态网络的输入权值随意性太大、中间状态数量庞大、关键参数凑试决定等问题,运用灰色关联度计算属性间的相关性从而确定输入权值。采用聚类算法简化中间状态,并用坐标轮换法搜索最佳的深度网络层数和储备池个数,对算法进行改进。通过UCI标准数据集的实验,发现改进后的算法提升了预测精度和速度。采用改进的深度回声网络预测卷烟厂空调负荷,通过当前时刻的内外部条件,解决由于负荷数据周期性波动所造成的预测效率低的问题,及时准确地预测出了下一时刻的空调负荷,提前对冷水机组的运行策略进行了调节,从而达到空调节能的目的。

In view of the problems such as too much randomness of input weights,the large number of intermediate states and the determination of key parameters by trial and error in the deep echo state network,this paper uses the grey correlation degree to calculate the correlation between the attributes to determine the input weights.Then,the clustering algorithm is used to simplify the intermediate states,and the coordinate rotation method is used to improve the algorithm by searching for the optimal depth network layers and the number of the reserve pools.Through the experiment of UCI standard data set,the improved algorithm in this paper improves the accuracy and speed of the prediction.Finally,the improved deep echo network is used to predict the air conditioning load of the cigarette factory.Through the internal and external conditions of the current moment,the low prediction efficiency caused by the periodic fluctuation of the load data is solved.The air conditioning load of the next moment is accurately predicted in time,and the operation strategy of the chiller is adjusted in advance to achieve the purpose of air conditioning energy saving.