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šŸ“˜ LMS AI - Chat with Excel Template

1. Overview

This video introduces a unique research-support template under LMS AIĀ called Chat with Excel, which helps researchers analyze large datasets stored in Excel sheets. Since most research outcomes (results tables, experimental datasets, simulations, measurements, and performance metrics) are maintained in spreadsheets, manual analysis becomes difficult when the dataset is large or contains multiple parameters.

The LMS AI Chat with ExcelĀ template solves this problem by allowing users to upload an Excel file and ask questions directly about the data. The tool then generates structured analytical insights, statistical summaries, and interpretation in an easy-to-understand academic format.

2. LMS AI Template Introduced: Chat with Excel

2.1 Purpose

The Chat with ExcelĀ template is designed to:

  • Read Excel datasets uploaded by the user

  • Interpret parameters and column meanings

  • Summarize findings from the dataset

  • Identify trends, comparisons, and best-performing methods

  • Provide statistical insights (min/max/range/variation)

  • Support research writing with conclusions and future research questions

3. Step-by-Step Workflow Shown in the Video

Step 1: Open LMS Solution Website

The user visits: lms solution.net.in

Step 2: Navigate to LMS AI

Inside the website, the user selects:

  • LMS AI

  • Then chooses the template Chat with Excel

Step 3: Upload Excel File

The dashboard provides:

  • ā€œChoose fileā€ option

  • Upload button

In the video, an Excel file named ā€œpowerā€Ā is uploaded.It contains columns such as:

  • Irradiance / irradiation

  • Power (theoretical / output)

  • Efficiency

  • Different methods (comparative methods)

  • Time-based readings or test cases

Step 4: Ask Questions in the ā€œQuestionsā€ Tab

After upload, the user goes to the QuestionsĀ tab and types queries such as:

  • ā€œExplain the resultsā€

  • ā€œSummarize the findingsā€

  • ā€œWhich method gives the highest efficiency?ā€

  • ā€œWhat is the range of power output?ā€

The template responds with a detailed analysis.

4. Type of Outputs Generated by LMS AI (Chat with Excel)

4.1 Results Explanation (Interpretation)

LMS AI explains what the dataset indicates, such as:

  • How efficiency varies across methods

  • How power output changes with irradiance

  • Which approach gives superior performance

  • What trends are visible across time or test conditions

4.2 Comparative Findings

The tool identifies:

  • Best-performing method (highest efficiency / highest power)

  • Worst-performing method (lowest output / poor efficiency)

  • Performance ranking based on selected metrics

4.3 Statistical Insights

LMS AI can generate:

  • Maximum and minimum values for each parameter

  • Average/typical performance range (where applicable)

  • Variability of each column

  • Which parameter shows strongest fluctuation

  • Which parameter is stable

4.4 Structured Conclusion

After analysis, LMS AI provides:

  • A clear conclusion describing what the data highlights

  • Key outcomes in a research-friendly tone

  • Statements that can be directly used in result analysis sections

5. Research Extension Feature

A key specialty highlighted in your video is:

āœ… LMS AI doesnā€™t stop at analysis ā€” it helps users extend their research.

After analyzing the Excel data, it generates possible research questionsĀ such as:

  • ā€œWhat additional parameters should be included in future studies?ā€

  • ā€œHow can performance analysis be made more comprehensive?ā€

When users ask these questions, LMS AI suggests additional variables like:

  • Temperature effects

  • Load variability

  • Material properties

  • System configuration

  • Design parameters

  • Environmental factors

  • Long-term performance metrics

This helps researchers improve the scope of their analysis and strengthen the discussion and future work section.

6. Academic Use Cases of LMS AI Chat with Excel

This template is useful for:

6.1 Simulation Result Analysis

  • MATLAB/Simulink exported results

  • Controller performance comparisons

  • MPPT / inverter / microgrid metrics tracking

6.2 Experimental Data Interpretation

  • Sensor readings

  • Efficiency vs load datasets

  • Power vs irradiance datasets

  • Harmonic/THD performance tables

6.3 Literature Review Data Extraction

  • Comparative tables

  • Benchmark result consolidation

  • Method vs metric mapping

6.4 Thesis & Paper Writing Support

  • Results and discussion drafting

  • Writing conclusions from data

  • Identifying research gaps using dataset patterns

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