This paper focuses on the application of computational intelligence techniques for optimizing energy consumption in smart buildings. With the increasing demand for sustainable and efficient energy management, computational intelligence offers innovative solutions to address the complexities involved in energy optimization. The study explores various computational intelligence approaches, including machine learning, evolutionary algorithms, and neural networks, which are employed to analyze and predict energy consumption patterns. The integration of these techniques allows for the development of intelligent systems capable of real-time energy monitoring and control, ultimately leading to reduced energy consumption and improved sustainability. The paper further discusses the challenges and limitations associated with the implementation of computational intelligence in smart buildings and proposes potential future research directions to enhance the effectiveness and efficiency of energy optimization strategies.
Smith, E. Computational Intelligence for Energy Optimization in Smart Buildings. Information Sciences and Technological Innovations, 2021, 3, 18. https://doi.org/10.69610/j.isti.20210417
AMA Style
Smith E. Computational Intelligence for Energy Optimization in Smart Buildings. Information Sciences and Technological Innovations; 2021, 3(1):18. https://doi.org/10.69610/j.isti.20210417
Chicago/Turabian Style
Smith, Emily 2021. "Computational Intelligence for Energy Optimization in Smart Buildings" Information Sciences and Technological Innovations 3, no.1:18. https://doi.org/10.69610/j.isti.20210417
APA style
Smith, E. (2021). Computational Intelligence for Energy Optimization in Smart Buildings. Information Sciences and Technological Innovations, 3(1), 18. https://doi.org/10.69610/j.isti.20210417
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