Hybrid intrahour DNI forecast model based on DNI measurements and sky-imaging data

Karout, Youssef; Thil, Stéphane; Eynard, Julien; Guillot, Emmanuel; Grieu, Stéphane

doi:10.1016/j.solener.2022.11.032

Cited by 13 publications

(6 citation statements)

References 34 publications

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“…The proposed image-based model, inspired by the forecast model developed by Karout et al [24], is a hybridization between the persistence model and an LSTM model. Its global architecture is shown in Figure 13.…”

Section: Image-based Forecastsmentioning

confidence: 99%

“…The developed reactor model is inspired by the work of Boujjat et al [23], who computed the production of a solar biomass gasifier over one year to assess its allothermal-autothermal hybridization. In addition, for the first time, DNI forecasts-perfect forecasts, smart persistence forecasts or image-based forecasts [24]-are part of the control framework, in order to achieve better smoothing of the disturbances through the day, thus taking full advantage of a predictive approach.…”

Section: Introductionmentioning

confidence: 99%

“…with The interested reader is referred to [24] for details about the image processing block. (12) where n obs is the number of observations, DNI is the measured DNI and DNI is the predicted DNI.…”

mentioning

confidence: 99%

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Model-Based Predictive Control of a Solar Hybrid Thermochemical Reactor for High-Temperature Steam Gasification of Biomass

et al. 2023

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The present paper deals with both the modeling and the dynamic control of a solar hybrid thermochemical reactor designed to produce syngas through the high-temperature steam gasification of biomass. First, a model of the reactor based on the thermodynamic equilibrium is presented. The Cantera toolbox is used. Then, a model-based predictive controller (MPC) is proposed with the aim of maintaining the reactor’s temperature at its nominal value, thus preserving the reactor’s stability. This is completed by adjusting the mirrors’ defocusing factor or burning a part of the biomass to compensate for variations of direct normal irradiance (DNI) round the clock. This controller is compared to a reference controller, which is defined as a combination of a rule-based controller and an adaptive proportional–integral–derivative (PID) controller with optimized gains. The robustness of the MPC controller to forecast errors is also studied by testing different DNI forecasts: perfect forecasts, smart persistence forecasts and image-based forecasts. Because of a high optimization time, the Cantera function is replaced with a 2D interpolation function. The results show that (1) the developed MPC controller outperforms the reference controller, (2) the integration of image-based DNI forecasts produces lower root mean squared error (RMSE) values, and (3) the optimization time is significantly reduced thanks to the proposed interpolation function.

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Section: Image-based Forecastsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model-Based Predictive Control of a Solar Hybrid Thermochemical Reactor for High-Temperature Steam Gasification of Biomass

et al. 2023

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“…In their study [ 8 ], Youssef Karout et al introduced a hybrid model integrating DNI (Direct Normal Irradiance) measurements with sky-imaging data. This research presents a model specifically designed for intrahour forecasting of DNI, with horizons spanning 5, 10, and 15 min.…”

Section: Introductionmentioning

confidence: 99%

Sky Image Classification Based on Transfer Learning Approaches

Hernández-López,

Travieso-González,

Ajali-Hernández

2024

Sensors

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Cloudy conditions at a local scale pose a significant challenge for forecasting renewable energy generation through photovoltaic panels. Consequently, having real-time knowledge of sky conditions becomes highly valuable. This information could inform decision-making processes in system operations, such as determining whether conditions are favorable for activating a standalone system requiring a minimum level of radiation or whether sky conditions might lead to higher energy consumption than generation during adverse cloudy conditions. This research leveraged convolutional neural networks (CNNs) and transfer learning (TL) classification techniques, testing various architectures from the EfficientNet family and two ResNet models for classifying sky images. Cross-validation methods were applied across different experiments, where the most favorable outcome was achieved with the EfficientNetV2-B1 and EfficientNetV2-B2 models boasting a mean Accuracy of 98.09%. This study underscores the efficacy of the architectures employed for sky image classification, while also highlighting the models yielding the best results.

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“…Hybrid model for intra hour DNI forecasting (5, 10, and 15 min). This hybrid technique uses a knowledge-based methodology for clear-sky DNI predictions using DNI data and a machine learning algorithm [27]. As the primary input, the proposed model employs a series of time-dependent irradiance estimates near the target location.…”

Section: Introductionmentioning

confidence: 99%

Novel and Efficient Hybrid Deep Learning Approach for Solar Photovoltaic Power Forecasting Considering Meteorological Parameters

Aman,

Rizwan,

Kumar

2023

Preprint

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The power generation from photovoltaic plants depends on varying meteorological conditions. These meteorological conditions such as solar irradiance, temperature, and wind speed, are non-linear and stochastic thus affect estimation of photovoltaic power. Accurate estimation of photovoltaic power is essential for enhancing the functioning of solar power installations. The paper aims to develop a novel deep learning based photovoltaic power forecasting model on different weather conditions. The proposed model utilizes a two-stage deep learning framework for accurate solar power forecasting, which combines the long short-term memory (LSTM) and convolutional neural network (CNN) deep learning architectures. The key role of CNN layer is to identify the weather conditions, i.e., sunny, cloudy and rainy while the LSTM layer learns the patterns of solar power generation that depend on weather variations to estimate photovoltaic power. The proposed hybrid models consider meteorological factors, such as wind speed, sun irradiations, temperature, and humidity, including cloud cover and UV index to provide precise solar power forecasting. The presented hybrid model, a Root Mean Square Error of 0.0254, 0.03465 and 0.0824, Mean Square Error of 0.000645, 0.00120 and 0.00679, R2 of 0.9898, 0.9872 and 0.9358, Mean Average Error of 0.0163 and 0.0236 and 0.2521 for sunny, cloudy and rainy weather conditions respectively. The results demonstrate that presented deep learning based novel solar photovoltaic (SPV) power forecasting model can accurately forecast solar power based on instantaneous changes in generated power patterns, and aid in the optimization of PV power plant operations. The paper presents an effective methodology for forecasting solar power that can contribute to the improvement of solar power generation and management.

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Hybrid intrahour DNI forecast model based on DNI measurements and sky-imaging data

Cited by 13 publications

References 34 publications

Model-Based Predictive Control of a Solar Hybrid Thermochemical Reactor for High-Temperature Steam Gasification of Biomass

Model-Based Predictive Control of a Solar Hybrid Thermochemical Reactor for High-Temperature Steam Gasification of Biomass

Sky Image Classification Based on Transfer Learning Approaches

Novel and Efficient Hybrid Deep Learning Approach for Solar Photovoltaic Power Forecasting Considering Meteorological Parameters

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