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A hybrid model approach for accurate hourly forecasting of heating energy consumption for different types of urban buildings

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Abstract

Heating energy is a significant component of building energy consumption and is mostly derived from fossil fuels. Predicting the amount of energy used for building heating is a critical first step in improving the energy consumption situation, particularly in developed and emerging nations, given the construction sector’s significant contribution to energy consumption. In order to anticipate heating energy consumption in various building types on an hourly basis, this work aims to develop a hybrid model that makes use of hybridization capabilities. XGBoost, HGBoost, CatBoost, and LightGBoost are four boosting algorithms that were combined with the GWO algorithm to create four different hybrid models. Optimizing and modifying the boosting algorithms’ hyperparameters was the aim of this hybridization. The case study’s findings demonstrated the appropriate accuracy of the suggested methodology for hourly heating energy consumption forecast. The XGBoost-GWO hybrid model has the best evaluation index values in commercial and residential structures, whereas the CatBoost-GWO hybrid model has the best evaluation index values in big venue buildings, according to the research findings. Thus, these models are recommended for predicting heating energy usage on an hourly basis.

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Data availability

No datasets were generated or analysed during the current study.

Abbreviations

ANN:

Artificial Neural Network

CatBoost:

Categorical Boosting

DBT:

Dry Bulb Temperature

DL:

Deep Learning

DNN:

Deep Neural Network

RF:

Random Forest

GWO:

Gray Wolf Optimization

HGBoost:

Histogram Gradient Boosting

LightGBoost:

Light Gradient Boosting

MLP:

Multilayer Perceptron

ML:

Machine Learning

AI:

Artificial Intelligence

n:

Number of observations

\(\:{\widehat{\text{o}}}_{\text{i}}\) :

The ith real value

\(\:\stackrel{-}{\text{o}}\) :

The mean of the data points

\(\:{\text{o}}_{\text{i}}\) :

The ith real value

PDF:

Probability Density Function

GBDT:

Gradient Boosting Decision Tree

SR:

Solar Radiation

SVR:

Support Vector Regression

WBT:

Wet Bulb Temperature

WS:

Wind Speed

XGBoost:

Extreme Gradient Boosting

DT:

Decision Tree

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  1. Shandong Huayu Institute of Technology, Dezhou, Shandong, 253000, China

    Wenguo Chen & Mengmeng Xu

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  1. Wenguo Chen
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All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by” Wenguo Chen and Mengmeng Xu”. Also. the first draft of the manuscript was written by Wenguo Chen. Mengmeng Xu commented on previous versions of the manuscript.

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Correspondence to Wenguo Chen.

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Chen, W., Xu, M. A hybrid model approach for accurate hourly forecasting of heating energy consumption for different types of urban buildings. SIViP 19, 1177 (2025). https://doi.org/10.1007/s11760-025-04801-5

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