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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62609
Volume:
10 |
5 |
May 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
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.
Electronics and Telecommunication
GH Raisoni College of Engineering and Management
Electronics and Telecommunication
GH Raisoni College of Engineering and Management
HR
Aldex Energies
Electronics and Telecommunication
GH Raisoni College of Engineering and Management
Electronics and Telecommunication
GH Raisoni College of Engineering and Management
As solar energy adoption continues to grow, companies need smarter, data-driven ways to make decisions. This project, Solar Energy Service Sales Analysis, focuses on studying real sales data to find useful insights that can help businesses improve their performance. Instead of just looking at raw data, the aim was to transform it into meaningful information that supports better planning and daily operations. During the analysis, clear patterns were identified, such as factors influencing revenue, customer behavior , and the performance of different regions. These insights help businesses plan resources more effectively and design targeted marketing strategies. Rather than relying on assumptions, companies can now make decisions based on actual data. The project also highlights the importance of data analytics in the solar industry. It not only helps businesses understand their current performance but also allows them to predict future trends. With a better understanding of what works and what doesn’t, companies can grow more efficiently and make informed choices. Overall, this project shows that in a fast-changing field like solar energy, businesses that invest in data analysis will have a strong advantage. They can make quicker, smarter decisions and build a more sustainable future.
KEYWORDS
The Solar Energy Service Sales Analysis project shows how useful raw data can become when it is managed properly. By using tools like Power BI, PostgreSQL, Python, and Microsoft Excel, the project improves data quality, automates repetitive tasks, and reduces the time needed to create reports. This helps decision-makers access information quickly and act with greater confidence. Another important benefit of the project is its ability to identify possible system faults and performance issues early. By using trend analysis and predictive methods, problems can be detected before they become serious. This allows timely maintenance, reduces downtime, and helps solar systems operate more efficiently over time.
1. AI-based predictive maintenance of solar photovoltaics systems: a comprehensive review:Authors: Rohan Vijay Vichare, Sachin Ramnath. Gaikwad Journal: Energy Informatics (2025) DOI: https://doi.org/10.1186/s42162-025-00594-6 2. Trend-based predictive maintenance and fault detection analytics for photovoltaic power plants: Authors: Marangis D., Livera A., Tziolis G., Makrides G., Kyprianou A., Georghiou G.E. Journal: Solar RRL (2024). DOI: https://doi.org/10.1002/solr.202400473 3. Development of prescriptive maintenance methodology for maintenance cost minimization of photovoltaic systems:Authors: Kothona D., Panapakidis I.P., Christoforidis G.C. Journal: Solar Energy (2024) DOI: https://doi.org/10.1016/j.solener.2024.112402 4. A Systematic Framework for Reconciling Solar Energy Production Models with Operational Data: Authors: Sahil Shah, Aravind Reddy Boozula, Radhika Girish Lampuse, Jigar J. Thakkar. Journal: European Journal of Energy Research (2025) DOI: https://doi.org/10.24018/ejenergy.2025.5.4.168
Publication Date
5/6/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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