TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA-LSTM Forecasting model

TL;DR

This paper introduces a hybrid deep learning model combining TRIZ, GWO, SARIMA, and LSTM to improve urban building energy consumption prediction accuracy, achieving a 15% reduction in error and aiding sustainable development.

Contribution

It presents a novel hybrid model integrating TRIZ innovation theory with GWO, SARIMA, and LSTM for more accurate energy consumption forecasting in urban buildings.

Findings

15% reduction in prediction error compared to existing models

Effective balance between energy efficiency, cost, and comfort achieved

Model demonstrates robustness across different conditions

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.