Article: Building energy efficiency intelligent scheduling integrating big data analysis and artificial intelligence Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.48 pp.1 - 25 Abstract: Building energy efficiency management is crucial for sustainable development amid global energy challenges. This study integrates big data analytics and artificial intelligence to develop an intelligent scheduling system for building energy optimisation. Using long short-term memory (LSTM) networks, a deep learning model was trained on multi-source data including energy consumption, weather forecasts, and pedestrian flow, achieving over 95% prediction accuracy. The system dynamically adjusts building equipment operations based on predictive outcomes, reducing overall energy consumption by 20%. Experimental results demonstrate significant economic benefits and enhanced energy efficiency. The research also explores broader applications of AI in energy management, such as equipment failure prediction and performance evaluation. This work provides a novel technological pathway for green building development and supports global sustainability goals. Inderscience Publishers - linking academia, business and industry through research

Title: Building energy efficiency intelligent scheduling integrating big data analysis and artificial intelligence

Authors: Ming Qiu

Addresses: School of Institute of Internet of Things Technology, Zhengzhou Urban Construction Vocational College, Gongyi, 451200, China

Abstract: Building energy efficiency management is crucial for sustainable development amid global energy challenges. This study integrates big data analytics and artificial intelligence to develop an intelligent scheduling system for building energy optimisation. Using long short-term memory (LSTM) networks, a deep learning model was trained on multi-source data including energy consumption, weather forecasts, and pedestrian flow, achieving over 95% prediction accuracy. The system dynamically adjusts building equipment operations based on predictive outcomes, reducing overall energy consumption by 20%. Experimental results demonstrate significant economic benefits and enhanced energy efficiency. The research also explores broader applications of AI in energy management, such as equipment failure prediction and performance evaluation. This work provides a novel technological pathway for green building development and supports global sustainability goals.

Keywords: big data analytics; artificial intelligence; building energy efficiency; intelligent scheduling; deep learning.

DOI: 10.1504/IJICT.2026.153550

International Journal of Information and Communication Technology, 2026 Vol.27 No.48, pp.1 - 25

Received: 28 Sep 2025
Accepted: 29 Oct 2025
Published online: 13 May 2026 *

Title: Building energy efficiency intelligent scheduling integrating big data analysis and artificial intelligence

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