Optimizing business strategies for energy through machine learning involves using predictive analytics for accurate energy demand and price forecasting, enhancing operational efficiency through resource optimization and predictive maintenance, and optimizing renewable energy integration into the energy grid. This approach maximizes production, reduces costs, and ensures stability in energy supply. The novelty of integrating deep reinforcement learning (DRL) in energy management lies in its ability to adapt and optimize operational strategies in real-time, autonomously leveraging advanced machine learning techniques to handle dynamic and complex energy environments. The study’s outcomes demonstrate the effectiveness of DRL in optimizing energy management strategies. Statistical validity tests revealed shallow error values [MAE: 1.056 × 10(− 13) and RMSE: 1.253 × 10(− 13)], indicating strong predictive accuracy and model robustness. Sensitivity analysis showed that heating and cooling energy consumption variations significantly impact total energy consumption, with predicted changes ranging from 734.66 to 835.46 units. Monte Carlo simulations revealed a mean total energy consumption of 850 units with a standard deviation of 50 units, underscoring the model’s robustness under various stochastic scenarios. Another significant result of the economic impact analysis was the comparison of different operational strategies. The analysis indicated that scenario 1 (high operational costs) and scenario 2 (lower operational costs) both resulted in profits of $70,000, despite differences in operational costs and revenues. However, scenario 3 (optimized strategy) demonstrated superior financial performance with a profit of $78,500. This highlights the importance of strategic operational improvements and suggests that efficiency optimization can significantly enhance profitability. In addition, the DRL-enhanced strategies showed a marked improvement in forecasting and managing demand fluctuations, leading to better resource allocation and reduced energy wastage. Integrating DRL improves operational efficiency and supports long-term financial viability, positioning energy systems for a more sustainable future.

Optimizing business strategies for carbon energy management in buildings: a machine learning approach in economics and management
    Abstract
    1 Introduction
    2 Methodology
        2.1 Study framework
        2.2 Data collection and validity
        2.3 Operational cost
        2.4 Mathematical formulation for energy utilization optimization
        2.5 DRL
    3 Results and discussion
        3.1 Optimizing business strategies for carbon energy
            3.1.1 Statistical validity tests
            3.1.2 Sensitivity analysis
            3.1.3 Monte Carlo simulations
            3.1.4 Economic impact analysis
            3.1.5 Real-time simulation tests for DRL models
    4 Conclusion
    Acknowledgements 
    References

• Hong Zhang(Management School, China University of Mining and Technology (Beijing), Beijing, China, Shenhua Engineering Technology Co. Ltd, Beijing, China)
• Teeb Basim Abbas(Mechanical Power Techniques Engineering Department, College of Engineering and Technology, Al Mustaqbal University, 51001 Hilla, Babylon, Iraq)
• Yousef Zandi(Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
• Alireza Sadighi Agdas(Ghateh Gostar Novin Company, Tabriz 51579, Iran)
• Zahra Sadighi Agdas(Urban Design, University of Tehran, Tehran, Iran)
• Meldi Suhatril(Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia)
• Emad Toghroli(Department of Civil Engineering, Calut Company Holding, Melbourne 800, Australia)
• Awad A. Ibraheem(Central Labs, King Khalid University, AlQura’a, P.O. Box 960, Abha, Saudi Arabia)
• Anas A. Salameh(Department of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz University, 165, 11942 Al‑Kharj, Saudi Arabia)
• Hakim AL Garalleh(Department of Mathematical Science, College of Engineering, University of Business and Technology-Dahban, 21361 Jeddah, Saudi Arabia)
• Hamid Assilzadeh(Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai 600077, India, Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam, School of Engineering & Technology, Duy Tan University, Da Nang, Viet Nam, Faculty of Architecture and Urbanism, UTE University, Calle Rumipamba S/N and Bourgeois, Quito, Ecuador)