Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/2454 2018 2018 César Vallejo University, Trujillo, Peru Darío González González-Cruz César Vallejo University, Trujillo, Peru Franky Jiménez-Garcà Jiménez-García César Vallejo University, Trujillo, Peru; National University of San Marcos, Trujillo, Peru Javier Gamboa Gamboa-Cruzado César Vallejo University, Trujillo, Peru Edward R. Luna Victoria National University Micaela Bastidas of Apurímac, Apurímac, Peru María Limaú Bendezú Higher Colleges of Technology, United Arab Emirates Reem Attasi At the forefront of sustainable energy solutions lies renewable energy, particularly solar power. Nevertheless, the optimization of solar power systems necessitates comprehensive analytics, especially for proactive maintenance fault anticipation. This research evaluates data fusion techniques using both linear and non-linear regression models for predicting faults in solar power plants. The study begins with careful data preparation processes to ensure clean and harmonized data sets that include irradiation, temperature, historical fault records, and yield. Linear regression techniques provide insights into straightforward correlations while non-linear models go deep into complex relationships within the data. The results indicate positive outcomes demonstrating the potential of these fusion techniques as far as improving accuracy in fault prediction is concerned. These findings highlight the importance of refining data preparation prior to any fusion process and recommend further exploration into more advanced fusion methodologies. This paper helps advance proactive maintenance strategies for solar power plants thereby making this source of energy more dependable and resilient. 2024 2024 211 218 10.54216/FPA.140217 https://www.americaspg.com/articleinfo/3/show/2454