A Novel Forecasting Model for Solar Power Generation by a Deep Learning Framework With Data Preprocessing and Postprocessing

Phan, Quoc-Thang; Wu, Yuan‐Kang; Dzung, Phan Quoc; Lo, Hsin-Yen

doi:10.1109/tia.2022.3212999

Cited by 23 publications

(11 citation statements)

References 45 publications

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“…In this paper, with the output of downward solar short-wave radiation flux and other related meteorological elements obtained from numerical weather prediction model and observed power, radiation and meteorological data, the short-term PV power forecast models with different initial time (00 and 12 UTC) and different forecast duration (24,48, and 72 hours) are established by the random forest method. The validity of the forecasting model is verified by the correlation analysis of the daily, monthly and seasonal variations of power generation.…”

Section: Discussionmentioning

confidence: 99%

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Study on a hybrid solar power prediction model based on a numerical weather prediction and random forests

GUAN,

Zou

2024

Preprint

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Photovoltaic grid-connected applications pose challenges to the stable and safe operation of power systems. Solar power predicted by a hybrid model based on numerical weather prediction (NWP) and random forests (RF) in order to enable grid-connected planning to balance fluctuations. Using downward solar short-wave radiation flux, temperature, wind speed, and other meteorological parameters obtained from the numerical weather forecast model, PV power forecast models with different initial time and durations under various weather conditions by RF are established. The correlations between observed and predicted power in daily, monthly and seasonal change are analyzed and both two powers are compared under three different detailed weather types. The results show that compared with NWP-principle method, this hybrid NWP-RF method can simulate the change of solar power under different weather conditions in the middle-lower plain of the Yangtze River more accurately. This model has an acceptable amount of data computation and high prediction accuracy, so that it can apply to engineering.

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Section: Discussionmentioning

confidence: 99%

“…On the other hand, the method, combining ML with numerical model, can improve the accuracy of PV forecasting effectively [23][24] . Hence, in this study, a solar PV power prediction model is established by random forest based on numerical weather prediction model.…”

Section: Introductionmentioning

confidence: 99%

Study on a hybrid solar power prediction model based on a numerical weather prediction and random forests

GUAN,

Zou

2024

Preprint

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“…This approach enhances boundary delineation and enables endto-end training, thereby reducing the number of algorithm parameters. The encoder and decoder of SegNet are similar to the autoencoder equation and are expressed by (3). 𝑓 𝜃 (𝛼) denotes an encoder block, and 𝑔 𝜃′ (𝛽) denotes a decoder block.…”

Section: B Semantic Segmentationmentioning

confidence: 99%

“…From an environmental and economic perspective, solar photovoltaic (PV) generation is a superior renewable energy source. Therefore, large-scale solar PV power plant farms are being constructed globally [3], [4], with a projected increase of "This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2021R1I1A3049503 and No.…”

Section: Introductionmentioning

confidence: 99%

Solar PV Generation Prediction Based on Multisource Data Using ROI and Surrounding Area

Kim,

Suh

2024

IEEE Trans. Geosci. Remote Sensing

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This study introduces a lightweight hybrid solar PV generation prediction model operating on 1-hour intervals, utilizing remote sensing data to enhance power grid management. Multisource remote sensing data, including spatial features from infrared satellite images and temporal data from various hourly recorded datasets, capture spatiotemporal characteristics. The model defines and synthesizes regions of interest (ROI) and surrounding areas of ROI (ROIsurr) within satellite images to reduce computational load. Integration of image and numerical weather prediction (NWP) process modules ensures accurate prediction. Comparative analysis against five machine learning algorithms shows significant improvements, with up to a 33.7% decrease in mean absolute error (MAE) and a 19.51% decrease in root mean square error (RMSE). Additionally, the model consistently meets ASHRAE Guideline 14 standards and outperforms single-source data models. Experimentation highlights the effectiveness of smaller ROIs in enhancing predictive accuracy, demonstrating adaptability to climate variations. This lightweight multisource remote sensing-based hybrid model promises to guide smart grid operations and sustainable power grid systems, advancing remote sensing applications in renewable energy management.

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“…Some works in the literature use pre-defined pre-processing techniques [49], [53], [54], such as fuzzy logic or ANFIS. Other works focus on using their own pre-processing analysis steps [50]- [52], including relevant data manipulation: from normalizing and scaling data to principal component analysis and feature extraction. Most works go even further and present a framework for cleaning and preparing data in more clear and concise steps [50], [55]- [58].…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding Climate Change and Air Quality Over the Last Decade: Evidence From News and Weather Data Processing

Văduva,

Munteanu,

Oprea

et al. 2023

IEEE Access

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Climate change is a phenomenon that is sometimes denied or trivialized. However, in recent years, we have faced extreme phenomena such as fires, floods, excessive temperatures, etc. which affect our physical and mental condition and the environment, often leading to significant material damage. To understand these problems and highlight the meteorological and phenomenological changes encountered in the last decade, time series were web-scraped and analyzed from several open data sources: weather news broadcast in Romania, air quality, temperature, etc. The extraction and organization of data recorded between 2009 and 2023 are formulated as a framework that can be reproduced and replicated to continue the monitoring. The exploratory analysis of the categorical and numerical data highlights intricate patterns and correlations within meteorological conditions across regions and seasons. From temperature trends to air quality fluctuations, the study underscores the dynamic interplay of weather phenomena, paving the way for informed forecasting and deeper climate research. At the same time, data processing includes Latent Dirichlet Allocation, K-prototype clustering analysis, in addition to K-means clustering with dimensional reduction techniques, all of which are employed to further reveal the extreme phenomena in news and regions with higher occurrence. Therefore, in this paper, we propose a data processing framework for multiple datasets and analytics, extracting valuable information on climate change and identifying the exposed regions to extreme phenomena. INDEX TERMSclimate change; news; web scraping; NLP; data analysis; data clustering

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A Novel Forecasting Model for Solar Power Generation by a Deep Learning Framework With Data Preprocessing and Postprocessing

Cited by 23 publications

References 45 publications

Study on a hybrid solar power prediction model based on a numerical weather prediction and random forests

Study on a hybrid solar power prediction model based on a numerical weather prediction and random forests

Solar PV Generation Prediction Based on Multisource Data Using ROI and Surrounding Area

Understanding Climate Change and Air Quality Over the Last Decade: Evidence From News and Weather Data Processing

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