The existing forecasting methods rarely analyze the periodic characteristics of energy consumption independently.A short-term office building energy consumption prediction model(DLnet)is proposed to solve the problem of low efficiency in the utilization of periodic energy consumption data.Firstly,the period component of the energy consumption data is decomposed using STL,and the optimal period of the energy consumption data is searched by grid searching algorithm.Secondly,periodic block is constructed according to optimal period.Then,time-series block data is constructed according to the data shape of the Periodic block.Next,time-series block data and the Periodic block data are trained and learned using LSTM.Finally,the prediction results of the time-series block data and the Periodic block data are fused by linear regression.The fact demonstrates that the four prediction precision indicators of the proposed model are 7%,21%,25%,and 26%higher than those of the long short-term memory(LSTM)model.
关键词
时序块/周期块/最佳周期/STL/长短期记忆
Key words
time-series block/periodic block/optimal period/seasonal-trend decomposition procedures based on Loess/long short-term memory
维普
万方数据