用于建筑能耗预测的多尺度可解释时序预测网络模型OA

Accurate forecasting of building energy consumption is crucial for optimizing energy management,reducing operational costs,and achieving carbon neutrality goals.This study proposes a multi-scale interpretable temporal prediction network model(ITSFN),which enhances prediction accuracy and reliability through the collaborative optimization of long short-term temporal(LSTM)networks and Kolmogorov-Arnold networks(KAN).The model integrates temporal-environmental feature decoupling with a dynamic attention mechanism,explicitly decomposing time-series data into seasonal,trend,and residual components to construct a structured feature space.It employs a parallel architecture of gated recurrent units(GRU)and multi-head attention to model multi-scale features.Tested on an energy consumption dataset from a university building in a hot-summer/cold-winter region,ITSFN outperforms traditional models:it reduces the root mean square error(RMSE)of total energy consumption prediction by 13.9%compared to LSTM and decreases the RMSE of sub-item energy consumption prediction by 31.1%compared to Transformer.Additionally,ITSFN enhances the noise suppression coefficient to 0.89 through feature decoupling,achieves a local attention angle of 0.92 in mutation regions,and reduces over-smoothing by 29.6%compared to traditional methods.By quantifying feature contributions,the model reveals the evolutionary patterns of component weights,further validating its effectiveness and practical applicability.