Water Demand Prediction Model of University Park Based on BP-LSTM Neural Network

Accurate water demand prediction is essential for optimizing the daily operations of water treatment plants and pumping stations. To achieve accurate prediction of water demand for university campuses, this study utilizes real hourly water consumption data collected over 380 observation days from a water treatment plant located on a university campus in Zhenjiang, Jiangsu Province. Based on periodicity analysis of the original data through Fast Fourier Transform (FFT) and autocorrelation coefficients, the data were preprocessed and aggregated into two-hour intervals. The processed water consumption data, along with temporal information (month, day of the week, date, and hour) and weather conditions (daily average wind speed, maximum and minimum temperature), were used as model inputs. The first 352 days of data were utilized to train the model, followed by 14 days serving as the validation set and the final two weeks as the test set. A hybrid forecasting model for campus water demand was developed by integrating a Back Propagation (BP) neural network with a Long Short-Term Memory (LSTM) neural network. The model’s performance was compared with standalone BP, LSTM, and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Simulation results demonstrate that, compared to other models, the proposed BP–LSTM hybrid model achieves a reduction in Mean Absolute Percentage Error (MAPE) ranging from 4.4% to 15.8%, and a decrease in Root Mean Squared Error (RMSE) between 2.5% and 16.8%. These findings indicate that the BP–LSTM model offers higher prediction accuracy and greater reliability compared to traditional single-model approaches.