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

Farah Z. Najdawi; Ruben Villarreal

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

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摘要

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.

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来源
《Energy and Power Engineering》 |2023年第11期|353-362|共10页
作者
Farah Z. Najdawi; Ruben Villarreal;
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作者单位

Department of Material Science Engineering and Commercialization, Texas State University, San Marcos, USA;

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原文格式 PDF
正文语种英语
中图分类能源与动力工程;
关键词
Vector Autoregression Model; Hyperparameter Parameters; Augmented Dickey Fuller; Durbin Watson's Statistics;

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

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