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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM53329

SolarIQ: Smart Solar Energy Forecasting & Insights Dashboards

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM53329

Volume:

9 |

10 |

Oct 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM53329

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

IAP

Irfaan Asif Patel

PTBF

Prof. T. B. Faruki

MAAK

Mohammad Abrar Asif Khatri

ADS

Atharv Dilip Sawant

MAAH

Mohseen Ali Alisab Hawaldar

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Abstract

SolarIQ is an intelligent platform developed to enhance solar energy management through accurate forecasting and insightful data visualization. The system employs machine learning algorithms to predict solar power generation using historical datasets and real-time weather parameters such as temperature, humidity, and irradiance. The platform is built using Node.js and Express.js for efficient backend processing, with MySQL serving as the primary database to ensure secure and structured data management. Interactive dashboards display key performance metrics including energy output trends, efficiency rates, and predictive analytics, enabling users to make data-driven decisions and optimize solar resource utilization. Additional modules such as anomaly detection, predictive maintenance alerts, and performance comparison improve operational reliability. By combining artificial intelligence with real-time data integration, SolarIQ bridges the gap between renewable energy data and actionable insights. The system demonstrates how smart forecasting and user-centric dashboards can support sustainable energy practices and contribute to environmental efficiency.

KEYWORDS

Solar energy forecasting Machine learning Weather data analysis OpenWeatherMap API Data Visualization Predictive analytics.

Cite This Article

Irfaan Asif Patel, Prof. T. B. Faruki, Mohammad Abrar Asif Khatri, Atharv Dilip Sawant, Mohseen Ali Alisab Hawaldar, "SolarIQ: Smart Solar Energy Forecasting & Insights Dashboards," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 10, Oct 2025. https://doi.org/10.55041/IJSREM53329

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Publication Date

10/30/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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5

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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