Comparing The Performance of Algorithms Used for Data Analysis in Solar Energy Systems

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Original abstract

Solar energy is a vital sustainable solution, but its efficiency is heavily affected by environmental variability, making accurate performance prediction essential. This study evaluates several prominent algorithms for solar energy forecasting to enhance system optimization. A systematic comparison was conducted among Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Multiple Linear Regression (MLR). These techniques were analysed based on predictive accuracy, computational speed, and ease of application. Findings indicate that while traditional models offer simplicity, advanced deep learning architectures like LSTM provide superior capabilities in handling the non-linear and temporal dependencies of solar data. This research provides a strategic framework for researchers and engineers in selecting the most suitable algorithm for specific solar energy applications, effectively balancing the trade-offs between model complexity and forecasting precision.

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