Investigation of Applicability of Impact Factors to Estimate Solar Irradiance: Comparative Analysis Using Machine Learning Algorithms

Cha, Jaehoon; Kim, Moon Keun; Lee, Sanghyuk; Kim, Kyeong Soo

doi:10.3390/app11188533

Cited by 4 publications

(5 citation statements)

References 60 publications

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“…Finally, a recent and interesting study complementary to short-term forecasting evaluated, with several ML and DP algorithms, the influence of exogenous meteorological variables on solar irradiance [55]. The results showed that temperature and cloud amount are determinant impact factors affecting solar irradiance.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The results showed that temperature and cloud amount are determinant impact factors affecting solar irradiance. Finally, considering the characteristics of studies comparing the accuracy between ANN and LSTM models for short-term prediction, detailed in Table 1, there is a lack of a complete study that uses different exogenous meteorological input variables, some of them being determinants of solar irradiance [55], and also including different short-term prediction horizons that influence the model's accuracy [25,56].…”

Section: Background and Related Workmentioning

confidence: 99%

“…As mentioned in Section 1.1, the variables in the Reduced Input Set were selected because they have a greater impact on solar irradiance, complementing and validating the results of previous studies [55], from experimental analysis with ANN and LSTM models. Thus, in particular, these results contribute to PSPPG by validating and adding new and relevant information about the use of meteorological variables in short-term predictions with ANN and LSTM.…”

Section: Sets Of Input Variablesmentioning

confidence: 99%

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Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models

et al. 2022

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The use of renewable energies, such as Photovoltaic (PV) solar power, is necessary to meet the growing energy consumption. PV solar power generation has intrinsic characteristics related to the climatic variables that cause intermittence during the generation process, promoting instabilities and insecurity in the electrical system. One of the solutions for this problem uses methods for the Prediction of Solar Photovoltaic Power Generation (PSPPG). In this context, the aim of this study is to develop and compare the prediction accuracy of solar irradiance between Artificial Neural Network (ANN) and Long-Term Short Memory (LSTM) network models, from a comprehensive analysis that simultaneously considers two distinct sets of exogenous meteorological input variables and three short-term prediction horizons (1, 15 and 60 min), in a controlled experimental environment. The results indicate that there is a significant difference (p < 0.001) in the prediction accuracy between the ANN and LSTM models, with better overall prediction accuracy skill for the LSTM models (MAPE = 19.5%), except for the 60 min prediction horizon. Furthermore, the accuracy difference between the ANN and LSTM models decreased as the prediction horizon increased, and no significant influence was observed on the accuracy of the prediction with both sets of evaluated meteorological input variables.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Sets Of Input Variablesmentioning

confidence: 99%

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Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models

et al. 2022

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“…(16) However, the quality and number of input features used in ML-based GHI prediction models might have an impact on their accuracy and reliability. (17) In order to increase the precision and robustness of the prediction models, feature selection is the process of choosing the most pertinent and instructive input features for a specific prediction task. (18) The accuracy and dependability of forecasting solar irradiation can be improved by providing more information on the impact of feature selection on GHI prediction models.…”

Section: Introductionmentioning

confidence: 99%

Impact of feature selection on the prediction of global horizontal irradiation under ouarzazate city climate

Benchikh,

Jarou,

Roa

et al. 2024

Data and Metadata

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Ensuring accurate forecasts of Global Horizontal Irradiance (GHI) stands as a pivotal aspect in optimizing the efficient utilization of solar energy resources. Machine learning techniques offer promising prospects for predicting global horizontal irradiance. However, within the realm of machine learning,the importance of feature selection cannot be overestimated, as it is crucial in determining performance and reliability of predictive models. To address this, a comprehensive machine learning algorithm has been developed, leveraging advanced feature importance techniques to forecast GHI data with precision. The proposed models draw upon historical data encompassing solar irradiance characteristics and environmental variables within the Ouarzazate region, Morocco, spanning from 1st January 2018, to 31 December 2018, with readings taken at 60-minute intervals. The findings underscore the profound impact of feature selection on enhancing the predictive capabilities of machine learning models for GHI forecasting. By identifying and prioritizing the most informative features, the models exhibit significantly enhanced accuracy metrics, thereby bolstering the reliability, efficiency, and practical applicability of GHI forecasts. This advancement not only holds promise for optimizing solar energy utilization but also contributes to the broader discourse on leveraging machine learning for renewable energy forecasting and sustainability initiatives.

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“…For example, a recent study [28] demonstrated a cloud cover estimation from images taken by sky-facing cameras using a multi-level machine learning technique. Another recent study [29] explored the applicability of the impact factors to estimate solar irradiance using multiple machine learning algorithms such as support vector machines, a long short-term memory (LSTM) neural network, linear regression, and a multi-layer neural network. They found that the LSTM model provided the best prediction accuracy using weather data without installing and maintaining on-site solar irradiance sensors.…”

Section: Introductionmentioning

confidence: 99%

Approximating Shading Ratio Using the Total-Sky Imaging System: An Application for Photovoltaic Systems

Dhimish

Lazaridis

2022

Energies

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In recent years, a determined shading ratio of photovoltaic (PV) systems has been broadly reviewed and explained. Observing the shading ratio of PV systems allows us to navigate for PV faults and helps to recognize possible degradation mechanisms. Therefore, this work introduces a novel approximation shading ratio technique using an all-sky imaging system. The proposed solution has the following structure: (i) we determined four all-sky imagers for a region of 25 km2, (ii) computed the cloud images using our new proposed model, called color-adjusted (CA), (iii) computed the shading ratio, and (iv) estimated the global horizontal irradiance (GHI) and consequently, obtained the predicted output power of the PV system. The estimation of the GHI was empirically compared with captured data from two different weather stations; we found that the average accuracy of the proposed technique was within a maximum ±12.7% error rate. In addition, the PV output power approximation accuracy was as high as 97.5% when the shading was zero and reduced to the lowest value of 83% when overcasting conditions affected the examined PV system.

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Investigation of Applicability of Impact Factors to Estimate Solar Irradiance: Comparative Analysis Using Machine Learning Algorithms

Cited by 4 publications

References 60 publications

Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models

Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models

Impact of feature selection on the prediction of global horizontal irradiation under ouarzazate city climate

Approximating Shading Ratio Using the Total-Sky Imaging System: An Application for Photovoltaic Systems

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