Abstract:
With the advancement of global climate change and sustainable development goals, urban
building energy consumption optimization and carbon emission reduction have become the focus of
research. Traditional energy consumption prediction methods often lack accuracy and adaptability
due to their inability to fully consider complex energy consumption patterns, especially in dealing
with seasonal fluctuations and dynamic changes. This study proposes a hybrid deep learning model
that combines TRIZ innovation theory with GWO, SARIMA and LSTM to improve the accuracy of
building energy consumption prediction. TRIZ plays a key role in model design, providing innovative
solutions to achieve an effective balance between energy efficiency, cost and comfort by
systematically analyzing the contradictions in energy consumption optimization. GWO is used to
optimize the parameters of the model to ensure that the model maintains high accuracy under different
conditions. The SARIMA model focuses on capturing seasonal trends in the data, while the LSTM
model handles short-term and long-term dependencies in the data, further improving the accuracy of
the prediction. The main contribution of this research is the development of a robust model that
leverages the strengths of TRIZ and advanced deep learning techniques, improving the accuracy of
energy consumption predictions. Our experiments demonstrate a significant 15% reduction in
prediction error compared to existing models. This innovative approach not only enhances urban
energy management but also provides a new framework for optimizing energy use and reducing
carbon emissions, contributing to sustainable development.
