Work

Energy optimization design and implementation of building engineering based on intelligent decision-making algorithm

Year: 2025

Type: article

Abstract: This paper presents an intelligent decision-making algorithm that integrates Q-learning, CNN, and LSTM to optimize energy consumption in building design and operations. The proposed method is evaluated against GA, PSO, SVR, and LSTM, demonstrating superior performance in energy reduction, response time, and computational efficiency. Experimental results show that the method reduces energy consumption by up to 25% in residential buildings and responds efficiently even under high computational loads. The system is further enhanced through a hybrid edge-cloud architecture to support real-time decision-making. While the findings are promising, limitations include reliance on simulated environments and the need for broader real-world validation. The proposed approach offers valuable insights for energy-efficient building design and smart energy management.

Source: Engineering Research Express

DOI: 10.1088/2631-8695/ae091c

Author: Shaonan Li

Institution: Zhengzhou Railway Vocational & Technical College

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: hybrid

Estimated APC paid: $2,400