An office building energy prediction model was established,leveraging the LSTM neural network.The Adam gradient optimization algorithm was introduced to adaptively adjust the learning rate.The model was trained using operational parameters,meteorological parameters,and energy consumption data from 2020 to 2022 for an office building in Shanghai.The average relative error of the model was merely 4.73%.Compared with other commonly used neural network algorithms for building energy consumption prediction,it demonstrates superior accuracy.To ensure the reliability of the model and avoid abrupt declines in prediction results,a set of non-training data was utilized to verify the prediction error.By inputting building operation parameters and meteorological parameters from January to July 2023,the long-term energy consumption of the buildings was predicted.The experimental results indicate that the error of this model in long-term energy consumption prediction is only 4.26%.Consequently,it accurately assimilates patterns and trends within the sequence,thereby providing practical application value for predicting office building energy consumption and managing operations.
LSTMneural networkbuilding energy consumptionautocorrelationenergy consumption prediction