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Energy control technology is evolving rapidly, enabling energy consumption monitoring and optimization. Such an actual paradigm is the “smart home systems” which extend from microcontroller devices like the ESP8266 and accurate sensors like the PZEM 004T, which enforce a detailed energy information procure. These devices facilitate real-time data acquisition and can be perfectly integrated with IoT infrastructures and advanced AI algorithms. Backend uses Python based frameworks for deep learning models and predictive analytics (Tensor flow, Pytorch, Scikit-learn). The neural networks can be created with the Keras library for powerful modeling of data used in accurate energy usage forecasting. The backend gets integrated with databases such as MongoDB efficiently by using Flask or Django for storing alerts and usage trends. From a frontend perspective, new web technologies have more intuitive interfaces to present energy consumption data so that it is easy to click through. In addition, large language models (LLMs) are used to generate optimization reports and actionable energy-saving strategies, delivering intelligent and contextual insights.This integrated methodology involves leveraging on-device hardware-level sensing technologies through advanced software to transform how energy is monitored across various use-cases from ownership, enterprise, and industry accelerate energy management efforts while introducing new opportunities in smart city infrastructure and sustainable energy development projects.

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A Survey of Predictive Electric Vision Systems Using Deep Learning for Energy Optimization

  • 会议论文

  • 发表日期：

    2026年

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  • Jyotsna Vilas Barpute,
  • Aditya Rasal,
  • Santosh Narwad,
  • Vinay More,
  • Gurukul More

摘要

Energy control technology is evolving rapidly, enabling energy consumption monitoring and optimization. Such an actual paradigm is the “smart home systems” which extend from microcontroller devices like the ESP8266 and accurate sensors like the PZEM 004T, which enforce a detailed energy information procure. These devices facilitate real-time data acquisition and can be perfectly integrated with IoT infrastructures and advanced AI algorithms. Backend uses Python based frameworks for deep learning models and predictive analytics (Tensor flow, Pytorch, Scikit-learn). The neural networks can be created with the Keras library for powerful modeling of data used in accurate energy usage forecasting. The backend gets integrated with databases such as MongoDB efficiently by using Flask or Django for storing alerts and usage trends. From a frontend perspective, new web technologies have more intuitive interfaces to present energy consumption data so that it is easy to click through. In addition, large language models (LLMs) are used to generate optimization reports and actionable energy-saving strategies, delivering intelligent and contextual insights.This integrated methodology involves leveraging on-device hardware-level sensing technologies through advanced software to transform how energy is monitored across various use-cases from ownership, enterprise, and industry accelerate energy management efforts while introducing new opportunities in smart city infrastructure and sustainable energy development projects.