Work

A hybrid prediction model for heating load of buildings within residential communities considering occupancy rates

Year: 2024

Type: article

Abstract: Accurate load prediction for each building is crucial for the optimized control of heating networks in residential areas. However, current residential heating load feature selection mainly focuses on environmental parameters and building-related factors, neglecting the influence of occupancy rates on different residential buildings, which leads to lower prediction accuracy. This study aims to obtain accurate short-term heating load predictions for different residential buildings within residential communities. The impact of occupancy rates on heating load is analyzed from the perspectives of inter-apartment heat transfer and building zoning, and a novel method for quantifying occupancy rates is proposed. Based on this, an OGGWO-GRU hybrid model is introduced, which uses the Local Opposition-Learning Golden-Sine Grey Wolf Optimization (OGGWO) algorithm to adjust hyperparameters, enhancing the model’s prediction accuracy. Finally, real heating load data is employed to validate the superiority of the proposed hybrid model. The results show that after introducing quantified occupancy rates, the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of the hybrid prediction model decreased by approximately 7.92% and 25.99%, respectively. Compared to other prediction models, the proposed hybrid model achieves minimum reductions of 11.47% in MAPE and 8.42% in RMSE.

Source: Energy and Buildings

DOI: 10.1016/j.enbuild.2024.115220

Authors: Anjun Zhao, Mingrui Zhang, Wenbo Sun, Wei Quan, Fukang Sun

Institution: Xi'an University of Architecture and Technology

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,180