Training-Data Generation and Incremental Testing for Daily Peak Load Forecasting

Moon, Jihoon; Park, Sung Woo; Jung, Seungmin; Hwang, Eenjun; Rho, Seungmin

doi:10.1007/978-3-030-70296-0_59

Cited by 1 publication

(1 citation statement)

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“…It highlights their independent considerations within the model while acknowledging possible real-world interdependencies. TSCV was applied to the ranger model for implementing an online learning approach, addressing data shortage issues, reflecting recent solar irradiance patterns, and adjusting the weights of input variables [49,50]. TSCV concentrates on multiple forecast horizons for each testing set, as shown in Figure 3.…”

Section: Model Constructionmentioning

confidence: 99%

A Hybrid Ensemble Model for Solar Irradiance Forecasting: Advancing Digital Models for Smart Island Realization

Leem

et al. 2023

Electronics

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This study introduces HYTREM, a hybrid tree-based ensemble learning model conceived with the sustainable development of eco-friendly transportation and renewable energy in mind. Designed as a digital model, HYTREM primarily aims to enhance solar power generation systems’ efficiency via accurate solar irradiance forecasting. Its potential application extends to regions such as Jeju Island, which is committed to advancing renewable energy. The model’s development process involved collecting hourly solar irradiance and weather-related data from two distinct regions. After data preprocessing, input variables configuration, and dataset partitioning into training and testing sets, several tree-based ensemble learning models—including extreme gradient boosting, light gradient boosting machine, categorical boosting, and random forest (RF)—were employed to generate prediction values in HYTREM. To improve forecasting accuracy, separate RF models were constructed for each hour. Experimental results validated the superior performance of HYTREM over state-of-the-art models, demonstrating the lowest mean absolute error, root mean square error (RMSE), and normalized RMSE values across both regions. Due to its transparency and efficiency, this approach suits energy providers with limited computational resources. Ultimately, HYTREM is a stepping stone towards developing advanced digital twin systems, highlighting the importance of precise forecasting in managing renewable energy.

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Section: Model Constructionmentioning

confidence: 99%

A Hybrid Ensemble Model for Solar Irradiance Forecasting: Advancing Digital Models for Smart Island Realization

Leem

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

Training-Data Generation and Incremental Testing for Daily Peak Load Forecasting

Cited by 1 publication

References 15 publications

A Hybrid Ensemble Model for Solar Irradiance Forecasting: Advancing Digital Models for Smart Island Realization

A Hybrid Ensemble Model for Solar Irradiance Forecasting: Advancing Digital Models for Smart Island Realization

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