A Deep Learning-Based Solar Power Generation Forecasting Method Applicable to Multiple Sites

Seon Young Jang, Byung Tae Oh () and Eunsung Oh ()
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Seon Young Jang: Department of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10504, Gyeonggi-do, Republic of Korea
Byung Tae Oh: Department of Computer Engineering, Korea Aerospace University, Goyang-si 10504, Gyeonggi-do, Republic of Korea
Eunsung Oh: Department of Electrical Engineering, College of IT Convergence, Global Campus, Gachon University, Seongnam-si 13120, Gyeonggi-do, Republic of Korea

Sustainability, 2024, vol. 16, issue 12, 1-15

Abstract: This paper addresses the challenge of accurately forecasting solar power generation (SPG) across multiple sites using a single common model. The proposed deep learning-based model is designed to predict SPG for various locations by leveraging a comprehensive dataset from multiple sites in the Republic of Korea. By incorporating common meteorological elements such as temperature, humidity, and cloud cover into its framework, the model uniquely identifies site-specific features to enhance the forecasting accuracy. The key innovation of this model is the integration of a classifier module within the common model framework, enabling it to adapt and predict SPG for both known and unknown sites based on site similarities. This approach allows for the extraction and utilization of site-specific characteristics from shared meteorological data, significantly improving the model’s adaptability and generalization across diverse environmental conditions. The evaluation results demonstrate that the model maintains high performance levels across different SPG sites with minimal performance degradation compared to site-specific models. Notably, the model shows robust forecasting capabilities, even in the absence of target SPG data, highlighting its potential to enhance operational efficiency and support the integration of renewable energy into the power grid, thereby contributing to the global transition towards sustainable energy sources.

Keywords: convolutional neural network; deep learning; domain estimation; long short-term memory; machine learning; renewable; solar power generation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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