AI- driven optimization of energy consumption in smart residential complexes
Keywords:
artificial intelligence, energy optimization, smart buildings, machine learning, residential complexes, energy management systems, predictive analytics, neural networksAbstract
This article examines the application of artificial intelligence technologies for optimizing energy consumption in smart residential complexes. The study analyzes contemporary approaches to implementing machine learning algorithms, neural networks, and predictive analytics for managing energy resources in multi-apartment buildings. The research demonstrates that AI-driven systems can reduce energy consumption by 25-40% compared to traditional management methods. The article presents a comprehensive analysis of architectures for intelligent energy management systems, including integration with Internet of Things sensors, smart meters, and building automation systems. Particular attention is given to machine learning methods for forecasting energy demand, optimizing heating, ventilation, and air conditioning systems, and managing renewable energy sources. The study examines challenges associated with implementing AI solutions, including data privacy, system integration complexity, and the need for substantial initial investments. The results show that deep learning algorithms demonstrate the highest efficiency in predicting consumption patterns, while reinforcement learning methods are most effective for real-time optimization. The article also discusses the economic feasibility of implementing such systems, demonstrating payback periods of 3-5 years depending on building size and climatic conditions. Recommendations are provided for developers, building managers, and policymakers regarding the implementation of AI-based energy management systems in residential complexes.
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