International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
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ENERGY OPTIMIZATION USING AI AND IOT Yelgodu Hanifa1, V. Ramesh Babu2, Pasupuleti Jagadeesh 3, Yerra srihari4, Narayanapurapu Venkata Satya Ganesh5 1Student & AMRITA SAI INSTITUTE OF SCIENCE AND TECHNOLOGY
2Assistant Professor & AMRITA SAI INSTITUTE OF SCIENCE AND TECHNOLOGY 3Student & AMRITA SAI INSTITUTE OF SCIENCE AND TECHNOLOGY 4Student & AMRITA SAI INSTITUTE OF SCIENCE AND TECHNOLOGY 5Student & AMRITA SAI INSTITUTE OF SCIENCE AND TECHNOLOGY
---------------------------------------------------------------------***--------------------------------------------------------------------Keywords: Artificial Intelligence, Internet of Things, SVC, Abstract Regression, KNN
Energy optimization has become one of the most critical aspects of sustainable development in modern societies, driven by the increasing demand for energy resources and the growing concern over environmental sustainability. Traditional methods of energy management are increasingly being replaced by more intelligent systems that leverage advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT). This paper presents a solution for energy optimization using AI and IoT, which aims to enhance the efficiency of energy consumption while reducing wastage and ensuring effective resource allocation.
1.INTRODUCTION Energy optimization has become an increasingly critical issue in today’s world, where global energy consumption is continually rising due to population growth, urbanization, and technological advancements. As the demand for energy grows, the need to find more efficient ways to use available resources has become more pressing, especially in light of climate change and environmental concerns. The conventional methods of energy management, which rely on centralized control and fixed infrastructure, are becoming less effective in managing the increasing complexities of modern energy demands. In this context, the combination of Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as a powerful solution to improve energy efficiency. By leveraging AI’s capabilities in data analysis and IoT’s ability to interconnect devices, these technologies can optimize energy usage across various sectors such as homes, industrial systems, and smart grids, creating more sustainable and cost-effective solutions.
The system integrates IoT devices, such as sensors and communication platforms, with AI algorithms to monitor, analyze, and control energy usage in real-time. These IoT devices gather data from various energyconsuming appliances and devices, such as lights, air conditioners, and heating systems, and transmit this data to a centralized system for analysis. Through the use of AI techniques, including machine learning and data analytics, the system processes this data to identify patterns in energy usage, detect inefficiencies, and predict future energy demands. These insights are then used to optimize energy usage by automating control processes, such as adjusting the thermostat, switching off unused appliances, and scheduling the operation of devices based on peak and off-peak hours.
AI plays a crucial role in energy optimization through its ability to analyze vast amounts of data and make data-driven decisions. By utilizing machine learning and predictive analytics, AI systems can forecast energy demand and identify inefficiencies in real-time. These AIdriven algorithms analyze historical data, environmental conditions, and other relevant parameters to predict future energy needs with high accuracy. This allows energy providers to optimize supply and distribution, preventing overloading of grids and reducing energy wastage. For example, AI can predict peak demand periods and adjust energy distribution accordingly to minimize the need for additional energy generation. Furthermore, AI can enable adaptive energy management systems in buildings and homes, where it can automatically control appliances, lighting, heating, and cooling based on occupancy and environmental factors. In industrial settings, AI can optimize machinery operations, reduce
The core of this paper revolves around an implementation that combines IoT technology with machine learning models, such as K-Nearest Neighbors (KNN), Support Vector Machines (SVC), Logistic Regression, and Random Forest, to predict and optimize energy consumption in various scenarios. By collecting environmental and system data such as temperature, humidity, light intensity, and object presence, the system can predict optimal operational modes for energyconsuming devices, thereby reducing unnecessary energy expenditure.
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