Time Series-Based Photovoltaic Power Forecasting to Optimize Grid Stability

Seshadri, Parthasarathy; Perumal, T S Bagavat; Kumar, B. Ashok; Kavinmathi, G.; Senthilrani, S.

doi:10.1080/15325008.2022.2129871

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…Inputs such as numerical weather predictions (NWP), site‐specific monitoring data, and specific characteristics of the power plant are utilized, [ 17,18 ] leading to outputs like global horizontal irradiance (GHI) and direct normal irradiance (DNI), directly influencing the plant's production capacity. [ 19 ] The innovative downscaling methodology Global precipitation mission (GPM)‐ based multitemporal weighted precipitation analysis (GMWPA), which utilizes the GPM_IMERGDF Product and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM within an empirical distribution‐based frameworl (EDBF) Algorithm, underscores the vital role of high‐resolution spatial data in precipitation analysis, particularly in areas like Mainland China. [ 20 ] The precision of meteorological data critically determines the accuracy of these models, with their predictive efficacy declining amidst imprecise weather information.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains

Hanif,

Siddique,

et al. 2024

Advcd Theory and Sims

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Effective solar energy utilization demands improvements in forecasting due to the unpredictable nature of solar irradiance (SI). This study introduces and rigorously tests two innovative forecasting models across different locations: the Sequential Deep Artificial Neural Network (SDANN) and the Deep Hybrid Random Forest Gradient Boosting (RFGB). SDANN, leveraging deep learning, aims to identify complex patterns in weather data, while RFGB, combining Random Forest and Gradient Boosting, proves more effective by offering a superior balance of efficiency and accuracy. The research highlights the SDANN model's deep learning capabilities along with the RFGB model's unique blend and their comparative success over existing models such as eXtreme Gradient Boosting (XGBOOST), Categorical Boosting (CatBOOST), Gated Recurrent Unit (GRU), and a K‐Nearest Neighbors (KNN) and XGBOOST hybrid. With the lowest Mean Squared Error (147.22), Mean Absolute Error (8.77), and a high R2 value (0.80) in a studied region, RFGB stands out. Additionally, detailed ablation studies on meteorological feature impacts on model performance further enhance accuracy and adaptability. By integrating cutting‐edge AI in SI forecasting, this research not only advances the field but also sets the stage for future renewable energy strategies and global policy‐making.

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Section: Introductionmentioning

confidence: 99%