ArticleOpen Access http://dx.doi.org/10.26855/acc.2025.07.007
Research on Energy Efficiency Optimization Algorithms for Hotel IoT Systems Based on AI Load Forecasting
Chao Zhang
Agoda, Shanghai 200041, China.
*Corresponding author: Chao Zhang
Published: August 20,2025
Abstract
The rapid development of the hotel industry has led to increasing energy consumption, which has become a significant constraint on its sustainable development. The widespread application of Internet of Things (IoT) technology has provided new opportunities for energy management in hotels, while the potential of Artificial Intelligence (AI) in load forecasting and energy efficiency optimization is gradually being explored. This paper focuses on the research of energy efficiency optimization algorithms for hotel IoT systems based on AI load forecasting, aiming to achieve effective energy conservation through innovative algorithm design. This study first conducts an in-depth analysis of the current energy consumption status in the hotel industry, explores the application prospects of IoT technology in energy management, and elaborates on the importance of AI technology in load forecasting and energy efficiency optimization. In the literature review section, the research progress in hotel energy management, IoT technology application, AI load forecasting, and energy efficiency optimization algorithms is systematically reviewed, laying a theoretical foundation for subsequent research. The core of the research lies in the construction of an AI-based load forecasting model and energy efficiency optimization algorithm. By designing a reasonable neural network structure and combining historical load data and weather information as input parameters, high-precision load forecasting is achieved. On this basis, an energy efficiency optimization algorithm for hotel IoT systems is proposed, which inte-grates the technical accumulation of Fuji Electric in energy-saving control algorithms. By dynamically adjusting the operation strategies of equipment, energy consumption is significantly reduced. Experimental results show that the proposed algorithm has significant effects on energy optimization, with a 25% improvement in energy-saving effects compared to traditional methods.
Keywords
Hotel energy management; Internet of Things (IoT); Artificial Intelligence (AI); Load forecasting; Energy efficiency optimization; Energy-saving control algorithms; Neural networks; Dynamic adjustment; Energy consumption models; Green and sustainable development
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How to cite this paper
Research on Energy Efficiency Optimization Algorithms for Hotel IoT Systems Based on AI Load Forecasting
How to cite this paper: Chao Zhang. (2025) Research on Energy Efficiency Optimization Algorithms for Hotel IoT Systems Based on AI Load Forecasting. Advances in Computer and Communication, 6(3), 144-150.
DOI: http://dx.doi.org/10.26855/acc.2025.07.007