2021/10/12 by Salih Sarp, Sarp, S., Murat Kuzlu +7
Computer Science · Energy · Engineering · #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Photovoltaic System Optimization Techniques #Signal Processing (eess.SP) #Solar Radiation and Photovoltaics #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2110.09948
openalex publication_date 2021/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The use of solar photovoltaics (PV) energy provides additional resources to the electric power grid. The downside of this integration is that the solar power supply is unreliable and highly dependent on the weather condition. The predictability and stability of forecasting are critical for the full utilization of solar power. This study reviews and evaluates various machine learning-based models for solar PV power generation forecasting using a public dataset. Furthermore, The root mean squared error (RMSE), mean squared error (MSE), and mean average error (MAE) metrics are used to evaluate the results. Linear Regression, Gaussian Process Regression, K-Nearest Neighbor, Decision Trees, Gradient Boosting Regression Trees, Multi-layer Perceptron, and Support Vector Regression algorithms are assessed. Their responses against false data injection attacks are also investigated. The Multi-layer Perceptron Regression method shows robust prediction on both regular and noise injected datasets over other methods.