Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model

Alani, Omaima El; Abraim, Mounir; Ghennioui, Hicham; Ghennioui, Abdellatif; Ikenbi, Ilyass; Dahr, Fatima-Ezzahra

doi:10.1016/j.egyr.2021.07.053

Cited by 58 publications

(13 citation statements)

References 26 publications

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“…Comparing the research results of similar literatures, it can be seen that reference [10] adopts the prediction method combining numerical weather forecasting and ground-based cloud image processing. Reference [32] used cloudenhanced analysis combined with LSTM neural network to predict ultra-short-term photovoltaic forecasting method.…”

Section: Comparison and Analysis Of Algorithm Predictionmentioning

confidence: 92%

“…influence factors, resulting in large prediction errors. Reference [10] uses a prediction method combining numerical weather forecasting and ground-based cloud image processing, which improves the photovoltaic prediction accuracy to a certain extent but ignores the sudden change of photovoltaic power that may be caused by moving cloud clusters. The power prediction accuracy needs to be improved.…”

Section: Introductionmentioning

confidence: 99%

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An Optimal Algorithm for Renewable Energy Generation Based on Neural Network

Zhao¹,

Khan

Akhtar

et al. 2022

International Journal of Photoenergy

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Solar energy is a costless and readily available form of energy that has shown to be one of the cleanest and most plentiful renewable energy sources. Various large-scale solar photovoltaic (PV) facilities are being utilized to minimize pollution and carbon emissions generated by fossil energy in many nations across the world. The power sequence of PV is influenced by a variety of diverse variables, and it is very unpredictable and volatile. Unlike the distributed PVs, the centralized PVs have the same intensity and location. The obstruction of clouds causes minor variations in the output power of the PV, making the power forecasting more difficult. To solve the aforementioned difficulties, this article provides a new neural network-based technique for PV power optimization and forecasting. The first stage is to create a cloud trajectory tracking system based on cloud photos taken from the ground. Second, a cloud trajectory tracking-based irradiance coefficient prediction model was built. Then, to increase forecast accuracy, build an error correcting model. For verification, data from a centralized solar power station was used. The results show that the proposed algorithm has technological applications and may greatly improve prediction accuracy.

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Section: Comparison and Analysis Of Algorithm Predictionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

An Optimal Algorithm for Renewable Energy Generation Based on Neural Network

Zhao¹,

Khan

Akhtar

et al. 2022

International Journal of Photoenergy

View full text Add to dashboard Cite

show abstract

“…In 2015, Peng et al used support vector machine classifiers to identify cloud clusters from multiple TSI images and evaluated the essential height and movement of each cloud cluster [17][18][19][20][21]. In 2018, the German DLR Solar Energy Research Institute developed the WobaS system [22][23][24][25][26]. This system comprises 2-4 cloud cameras that are used to capture sky images.…”

Section: Methods For Cloud Monitoringmentioning

confidence: 99%

Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed

2022

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Cloud covering is an important factor affecting solar radiation and causes fluctuations in solar energy production. Therefore, real-time recognition and the prediction of cloud covering and the adjustment of the angle of photovoltaic panels to improve power generation are important research areas in the field of photovoltaic power generation. In this study, several methods, namely, the principle of depth camera measurement distance, semantic segmentation algorithm, and long- and short-term memory (LSTM) network were combined for cloud observation. The semantic segmentation algorithm was applied to identify and extract the cloud contour lines, determine the feature points, and calculate the cloud heights and geographic locations of the cloud shadows. The LSTM algorithm was used to predict the trajectory and speed of the cloud movement, achieve accurate and real-time detection, and track the clouds and the sun. Based on the results of these methods, the shadow area of the cloud on the ground was calculated. The recursive neural LSTM network was also used to predict the track and moving speed of the clouds according to the cloud centroid data of the cloud images at different times. The findings of this study can provide insights to establish a low-cost intelligent monitoring predicting system for cloud covering and power generation.

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“…To date, global solar PV capacity addition is expected to reach approximately 160 × 10 9 W [4], [5]. Morocco is among the Middle East and North African (MENA) countries that receive tremendous solar irradiation, with around 3000 hours of sunlight per year and approximately 5000 Wh/m 2 /day of irradiation, especially during summer [6].…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Analysis of PV Module Positions in a Solar PV Array Under Partial Shading Conditions

et al. 2023

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The irradiation intensity is the primary catalyst for energy production in photovoltaic (PV) systems. Nevertheless, it alleviates partial shading (PS) by inhibiting power production, primarily in habitable metropolitan areas, owing to surrounding objects or high soiling rates, particularly in dry and semi-arid climates. Hence, the PV module's location, orientation, and tilt angle significantly affect its performance and shorten its durability. Thus, this study undertakes a benchmark analysis of the two distinct panel positions' performance using PSIM and MATLAB software simulations and an actual practical test. A visual inspection of a solar power plant in Morocco's Green Energy Park is the origin of the survey concept; it observes that the soil density consistently covers certain PV panel spots. In this context, the solar energy data of an intelligent home deployed in the earlier-mentioned research platform has been deployed to emulate the performance of future buildings challenged with various obstacles. Therefore, the roof-mounted PV system satisfied the home's energy demands. In the first scenario, the PV module is in portrait mode. In the second case, they are in landscape orientation. The findings of this investigation demonstrate that standard PV panels produce more power when arranged in a landscape configuration than in a portrait configuration, exhibiting a discrepancy of up to 1010 Wh for a modest PV system. Finally, this study suggests appropriate orientation and design recommendations for standard and advanced solar modules, mainly those deployed in arid and heavily populated urban regions featuring severe shading constraints.

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Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model

Cited by 58 publications

References 26 publications

An Optimal Algorithm for Renewable Energy Generation Based on Neural Network

An Optimal Algorithm for Renewable Energy Generation Based on Neural Network

Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed

Comparative Performance Analysis of PV Module Positions in a Solar PV Array Under Partial Shading Conditions

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