The aim of this project is to find the best tool for analysing the smart home energy. In this project we are going to use two major tools called Power BI and real time energy monitoring which are open-source tools. In these tools we are going to insert the data of a smart home and analyse the difference in both the algorithms. By this, we can justify which tool is most essential for the present technical world. We also can find the user-friendly application by this project. Power BI and real-time energy monitoring system can analyse the data. This project contains the description about power BI and its methodology and how we are going to analyse the data. It also contains the description about real time energy monitoring, its methodology, analysis and algorithms for both the tools. By this we can analyse the energy optimization. We can predict the energy usage and if the optimization percentage is high then we can estimate the progress and we can also find the solutions to reduce the energy usage. We compare the scalability of both the algorithms after the analysis of the data by using different algorithms.

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Smart Home Energy Monitoring Using Power BI Algorithm with Real-Time Monitoring Algorithm

  • P. Rajyasri,
  • Dinesh Kumar

摘要

The aim of this project is to find the best tool for analysing the smart home energy. In this project we are going to use two major tools called Power BI and real time energy monitoring which are open-source tools. In these tools we are going to insert the data of a smart home and analyse the difference in both the algorithms. By this, we can justify which tool is most essential for the present technical world. We also can find the user-friendly application by this project. Power BI and real-time energy monitoring system can analyse the data. This project contains the description about power BI and its methodology and how we are going to analyse the data. It also contains the description about real time energy monitoring, its methodology, analysis and algorithms for both the tools. By this we can analyse the energy optimization. We can predict the energy usage and if the optimization percentage is high then we can estimate the progress and we can also find the solutions to reduce the energy usage. We compare the scalability of both the algorithms after the analysis of the data by using different algorithms.