What question did this study set out to answer?

This research aims to improve water demand forecasting and operational efficiency in building water supply systems.

May 6, 2026Buildings0 citationsOpen Access

A Hybrid PSO-GWO-BP Predictive Model for Demand-Driven Scheduling and Energy-Efficient Operation of Building Secondary Water Supply Systems

SZShu-Guang Zhu JYJing-Wen Yu XWX W Wang

Key Points

This research aims to improve water demand forecasting and operational efficiency in building water supply systems.
Developed a hybrid PSO-GWO-BP predictive model combining machine learning algorithms.
Used hourly water consumption data from a residential district for model training and evaluation.
Compared performance against standalone models such as ELM, SVM, LSTM, and GRU.
Achieved a predictive accuracy of 97.06%, outperforming all benchmark models.
Obtained the lowest mean absolute error, mean squared error, and root mean square error.
Enhanced operational management and efficiency in building water supply systems.

Abstract

Accurate forecasting of water demand enables optimized peak-load management, alleviating pressure during high-demand periods and improving the operational efficiency of urban secondary water supply systems—a critical component in the energy-efficient and sustainable operation of buildings. However, existing water demand prediction methods in some regions suffer from low accuracy and excessively long prediction cycles, posing challenges for real-time water scheduling in building-scale systems. To address these challenges, this study develops a hybrid predictive framework that integrates a BP neural network with the Gray Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms for enhanced parameter optimization. Using hourly water consumption data from a representative residential district, the proposed model is compared against standalone machine learning models—Extreme Learning Machines (ELM), Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Model performance is rigorously evaluated using the coefficient of determination, mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE). The PSO-GWO-BP hybrid model achieves a predictive accuracy of 97.06%, yielding the lowest MAE, MSE, RMSE, and MAPE, as well as the highest R among all models considered, thereby significantly outperforming the benchmark standalone models. Furthermore, the high-precision short-term prediction outputs enable dynamic regulation of secondary water tank refill thresholds, facilitating refined water allocation and enhanced operational management of building water supply systems. These findings demonstrate the considerable application potential of the proposed hybrid model in enhancing both water resource efficiency and energy utilization performance in the daily operation of green buildings, providing reliable technical support for intelligent and low-carbon building water supply management.

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