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Nwokolo, S.C., et al. (2022) Hybridization of Statistical Machine Learning and Numerical Models for Improving Beam, Diffuse and Global Solar Radiation Prediction. Cleaner Engineering and Technology, 9, Article 100529.
https://doi.org/10.1016/j.clet.2022.100529

has been cited by the following article:

• TITLE: Utilizing the Vector Autoregression Model (VAR) for Short-Term Solar Irradiance Forecasting

  AUTHORS: Farah Z. Najdawi, Ruben Villarreal

  KEYWORDS: Vector Autoregression Model, Hyperparameter Parameters, Augmented Dickey Fuller, Durbin Watson’s Statistics

  JOURNAL NAME: Energy and Power Engineering, Vol.15 No.11, November 6, 2023

  ABSTRACT: Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector Autoregression (VAR) model to forecast solar irradiance levels and weather characteristics in the San Francisco Bay Area. The results demonstrate a correlation between predicted and actual solar irradiance, indicating the effectiveness of the VAR model for this task. However, the model may not be sufficient for this region due to the requirement of additional weather features to reduce disparities between predictions and actual observations. Additionally, the current lag order in the model is relatively low, limiting its ability to capture all relevant information from past observations. As a result, the model’s forecasting capability is limited to short-term horizons, with a maximum horizon of four hours.