Transfer learning-based adaptive recursive neural network for short-term non-stationary building heating load prediction

Building energy consumption is a non-stationary time series, and its distribution law changes over time. Traditional machine-learning models are prone to model shift, which leads to a reduction of their prediction accuracy when applied to building energy consumption forecasting. Therefore, in this paper, an adaptive neural network framework is proposed for non-stationary building energy consumption prediction based on transfer learning, in which the training datasets are divided into the most dissimilar periods, and based on the transfer learning mechanism, the different periods of data are learned to obtain the minimum overall loss to improve the model generalization. Taking LSTM and GRU models as examples, adaptive long short-term memory (adaLSTM) and adaptive gated recurrent unit (adaGRU) building energy consumption prediction models are established. The models were trained and verified using heating load data from Xi'an, China. The results show that compared with LSTM model, the coefficient of determination, root mean square error, the coefficient of variation of the root mean squared error and mean absolute error of the adaLSTM model were improved by 0.61%, 37.78%, 38.05% and 30.69%, respectively, and the over-fitting degree was reduced by 227.7%. Compared with the traditional GRU model, the corresponding evaluation indexes of adaGRU were improved by 2.50%, 70.58%, 70.64% and 68.83%, respectively, and the over-fitting degree was improved by 505.7% points. The adaptive recurrent neural network framework proposed in this paper is a generalized approach which can be applied to other non-stationary time series prediction models.