Forecasting solar energy production in Spain: A comparison of univariate and multivariate models at the national level

Applied Energy, Год журнала: 2023, Номер 350, С. 121645 - 121645

Опубликована: Авг. 10, 2023

Renewable energies, such as solar power, offer a clean and cost-effective energy source. However, their integration into national electricity grids poses challenges due to dependence on climate geography. While numerous studies have focused time series, few specifically addressed the critical task of forecasting production at level. Accurate national-level is crucial for optimizing management, informing policy development, promoting environmental sustainability. This study aims address associated with significant variability in renewable its impact grid stability by improving accuracy existing approaches. To achieve this goal, we evaluate effectiveness univariate multivariate approaches series data from ESIOS (the Spanish System Operator). Our primary focus leveraging external variables, irradiance data. end, propose methodology integrate forecasts historical power plants Spain improve performance models. Subsequently, compare classical regression techniques state-of-the-art deep learning algorithms, presenting models three forecast horizons (1 h, 24 48 h). Finally, assess our best comparing them official ESIOS. findings indicate that best-performing are deep-learning approaches, which benefit incorporating forecasts, particularly longer (24 h h), avoid detrimental effects Hughes Phenomenon, seems hamper non-deep-learning forecasters. The top-performing models, based Convolutional Networks + Recurrent Neural Networks, outperform reducing mean absolute error 41% 47.58%, respectively.

Язык: Английский

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Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer

Case Studies in Thermal Engineering, Год журнала: 2024, Номер 60, С. 104743 - 104743

Опубликована: Июнь 24, 2024

In this study, eXtreme Gradient Boosting (XGBoost) and Light (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, mass product) dependent (energy consumption size reduction) established. For energy consumption, XGBoost demonstrates superior performance R2 0.9957 during training 0.9971 testing, alongside minimal MSE 0.0034 0.0008 testing phase indicating high predictive accuracy low error rates. Conversely, LGBM shows lower values (0.9061 training, 0.8809 testing) higher 0.0747 0.0337 reflecting poorer performance. Similarly, for shrinkage prediction, outperforms LGBM, evidenced by (0.9887 0.9975 (0.2527 0.4878 testing). comparative statistics showed that regularly outperformed LightGBM. game theory-based Shapley functions revealed temperature types most influential features model. These findings illustrate practical applicability LightGBM models in food operations towards optimizing conditions, improving quality, reducing consumption.

Язык: Английский

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Predicting Solar Proton Events of Solar Cycles 22–24 Using GOES Proton and Soft-X-Ray Flux Features

The Astrophysical Journal Supplement Series, Год журнала: 2024, Номер 270(1), С. 15 - 15

Опубликована: Янв. 1, 2024

Abstract Solar energetic particle (SEP) events and their major subclass, solar proton (SPEs), can have unfavorable consequences on numerous aspects of life technology, making them one the most harmful effects activity. Garnering knowledge preceding such by studying operational data flows is essential for forecasting. Considering only cycle (SC) 24 in our previous study, we found that it may be sufficient to utilize soft X-ray (SXR) parameters SPE forecasts. Here, report a catalog recording ≥10 MeV flux unit SPEs with properties, spanning SCs 22–24, using NOAA’s Geostationary Operational Environmental Satellite data. We an additional daily SXR statistics this period, employing test application machine learning (ML) prediction support vector (SVM) extreme gradient boosting (XGBoost). explore training models from two SCs, evaluating how transferable model might across different time periods. XGBoost proved more accurate than SVMs almost every considered, while also outperforming SWPC NOAA predictions persistence forecast. Interestingly, done SC produces weaker true skill statistic Heidke scores 2 , even when paired 22 or 23, indicating transferability issues. This work contributes toward validating forecasts long-spanning data—an understudied area SEP research should considered verify cross robustness ML-driven

Язык: Английский

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A multimodal deep learning approach for gravel road condition evaluation through image and audio integration

Transportation Engineering, Год журнала: 2024, Номер 16, С. 100228 - 100228

Опубликована: Фев. 2, 2024

This study investigates the combination of audio and image data to classify road conditions, particularly focusing on loose gravel scenarios. The dataset underwent binary categorisation, comprising segments capturing sounds corresponding images. Early feature fusion, utilising a pre-trained Very Deep Convolutional Networks 19 (VGG19) Principal component analysis (PCA), improved accuracy Random Forest classifier, surpassing other models in accuracy, precision, recall, F1-score. Late involving decision-level processing with logical disjunction conjunction gates (AND OR) individual classifiers for images based Densely Connected 121 (DenseNet121), demonstrated notable performance, especially OR gate, achieving 97% accuracy. late fusion method enhances adaptability by compensating limitations one modality information from other. Adapting maintenance identified conditions minimises unnecessary environmental impact. can help identify roads, substantially improving safety implementing precise strategy through data-driven approach.

Язык: Английский

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Machine learning of redundant energy of a solar PV Mini-grid system for cooking applications

Solar Energy, Год журнала: 2023, Номер 262, С. 111790 - 111790

Опубликована: Июнь 21, 2023

Язык: Английский

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Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series

Clean Technologies and Environmental Policy, Год журнала: 2024, Номер 26(12), С. 4405 - 4431

Опубликована: Апрель 20, 2024

Язык: Английский

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

Advanced Theory and Simulations, Год журнала: 2024, Номер 7(7)

Опубликована: Апрель 30, 2024

Abstract Effective solar energy utilization demands improvements in forecasting due to the unpredictable nature of irradiance (SI). This study introduces and rigorously tests two innovative models across different locations: Sequential Deep Artificial Neural Network (SDANN) Hybrid Random Forest Gradient Boosting (RFGB). SDANN, leveraging deep learning, aims identify complex patterns weather data, while RFGB, combining Boosting, proves more effective by offering a superior balance efficiency accuracy. The research highlights SDANN model's learning capabilities along with RFGB unique blend their comparative success over existing such as eXtreme (XGBOOST), Categorical (CatBOOST), Gated Recurrent Unit (GRU), K‐Nearest Neighbors (KNN) XGBOOST hybrid. With lowest Mean Squared Error (147.22), Absolute (8.77), high R 2 value (0.80) studied region, stands out. Additionally, detailed ablation studies on meteorological feature impacts model performance further enhance accuracy adaptability. By integrating cutting‐edge AI SI forecasting, this not only advances field but also sets stage for future renewable strategies global policy‐making.

Язык: Английский

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Application of multi-source data fusion on intelligent prediction of photovoltaic power

Solar Energy, Год журнала: 2024, Номер 277, С. 112706 - 112706

Опубликована: Июнь 21, 2024

Язык: Английский

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A method for accurate prediction of photovoltaic power based on multi-objective optimization and data integration strategy

Applied Mathematical Modelling, Год журнала: 2024, Номер 136, С. 115643 - 115643

Опубликована: Авг. 17, 2024

Язык: Английский

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A hybrid ensemble learning framework for zero-energy potential prediction of photovoltaic direct-driven air conditioners

Journal of Building Engineering, Год журнала: 2022, Номер 64, С. 105602 - 105602

Опубликована: Ноя. 24, 2022

Язык: Английский

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