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In the fabric of energy generation, solar power is the most promising clean energy solution as an alternative to non-renewable energy sources. However, solar power’s dependency on environmental factors adds uncertainty to energy production. In such a scenario, solar power forecasting provides an edge to mitigate this uncertainty and improves overall system stability. Recently, machine learning (ML) models have been extensively deployed for designing and forecasting solar power. However, data pre-processing, forecast horizon, and performance evaluation of ML algorithms have to be carefully evaluated to find an accurate model. This paper provides an empirical comparison of different generation ML models for solar power forecasting, which can help understand future research on which method to adopt, depending on the ML model’s strengths and weaknesses. Therefore, an effective forecasting method is designated in aspects such as performance errors, convergence time, and computational complexity. So, this work rates different ML models on error performance metrics and convergence time. Moreover, cross-fold validations and hyperparameter are also examined for the top five performing models for a comprehensive evaluation and to give more intuitive and calibrated insight into various stakeholders working in the solar power plant modeling field.

An Empirical Analysis of Machine Learning Algorithms for Solar Power Forecasting in a High Dimensional Uncertain Environment