International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The integration of the Semantic Kernel with OpenAI presents a novel framework for developing agentic artificial intelligence (AI) solutions for autonomous environmental control in smart homes. This approach leverages Semantic Kernel’s capabilities in natural language understanding, contextual reasoning, and task orchestration, combined with OpenAI’s advanced generative AI models. Together, these technologies enable the creation of intelligent agents capable of interpreting complex user commands, understanding contextual nuances, and autonomously managing dynamic environmental conditions within smart home ecosystems.
The solution meets the challenges of making real-time decisions and providing personalized user experiences in smart homes. Semantic Kernel allows the design of flexible AI agents that manage memory, interact with external APIs, and execute tasks efficiently. When combined with OpenAI, these agents acquire superior language processing and conversational skills, facilitating seamless interactions with users and other smart home devices. This leads to a smart home ecosystem where AI optimizes lighting, temperature, quality, and energy use based on user preferences and context.
A significant feature of this framework is its capacity to function autonomously while adjusting to user input and evolving scenarios. Semantic Kernel’s contextual memory facilitates tailored interactions by retaining user preferences and previous actions, enabling AI agents to modify their responses accordingly. For instance, the system can learn and adapt to a user’s preferred lighting and temperature settings throughout the day or respond to changes in weather or energy availability.
The framework includes design principles and implementation strategies for integrating Semantic Kernel with OpenAI in smart home environments. Practical examples show how these technologies can turn traditional smart homes into fully autonomous systems. Demonstrations cover scenarios like coordinating multiple devices for energy efficiency, adapting environmental control based on user activity, and AI-driven emergency responses for safety.

Data analysis, particularly predictive analysis, has seen significant development with the emergence of modern LLMs,
especially the latest GPT models. Nowadays, Generative AI is a turning point in modern industry. Many new LLMs have been available
during the last several years, but an essential case is how these models are changing: what is the direction of evolution, and what can we
expect from Gen AI shortly?
This study analyses different aspects of the evolution of predictive analysis systems using different LLMs: GPT 3.5 turbo, GPT-4, GPT-
4o, GPT-4o1 OpenAI GPT o1.
This report includes comparative studies of predictive analysis for construction structures conducted using a comparative reference
framework with different GPT models, reaching GPT-4o and GPT o1. The accuracy of the analysis on identical cases is compared. The
study also compares the cost and performance of the different models. Current research will be useful for scientists, researchers, industry
engineers, and businesses to estimate the cost and effectiveness of the GenAI solutions that they are going to implement and choose the right
LLM for their cases and