Evaluation of dimensionality reduction methods applied to numerical weather models for solar radiation forecasting

García-Hinde, Óscar; Terrén-Serrano, Guillermo; Hombrados-Herrera, Miguel Ángel; Gómez-Verdejo, Vanessa; Jiménez-Fernández, Silvia; Casanova-Mateo, C.; Sanz-Justo, J.; Martínez‐Ramón, Manel; Salcedo‐Sanz, Sancho

doi:10.1016/j.engappai.2017.12.003

Cited by 44 publications

(14 citation statements)

References 57 publications

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“…Image processing is an important factor in the performance of a solar forecasting algorithm [10] . In the context of machine learning and image processing, the feature extraction algorithm may be adapted to different applications in solar problems [11] , [12] . For instance, the solar forecasting horizon can be changed depending on the application.…”

Section: Value Of the Datamentioning

confidence: 99%

Girasol, a sky imaging and global solar irradiance dataset

et al. 2021

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The energy available in a microgrid that is powered by solar energy is tightly related to the weather conditions at the moment of generation. A very short-term forecast of solar irradiance provides the microgrid with the capability of automatically controlling the dispatch of energy. We propose a dataset to forecast Global Solar Irradiance (GSI) using a data acquisition system (DAQ) that simultaneously records sky imaging and GSI measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed the Girasol Machine (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on a solar tracker. The tracker updates its pan and tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal mount next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun’s positions, has been tagged with timestamps.

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Section: Value Of the Datamentioning

confidence: 99%

Girasol, a sky imaging and global solar irradiance dataset

et al. 2021

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“…Sun et al developed a new forecasting strategy for solar radiation (SR) time series based on ensemble mode decomposition and clustering algorithm [8]. Garcia-Hinde et al proposed a learning strategy for SR data regression based on input dimensionality reduction [9]. Bailek et al predicted diffuse SR measurement over the Algerian Sahara using an empirical approach [10].…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Learning Algorithm for Solar Radiation Components Forecasting Based on the Desired Tilt Angle of a Solar Energy System

Jallal¹,

Yassini²,

Chabaa³

et al. 2021

I2M

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Solar radiation components (SRC) forecasting with different tilt angles plays a key role for planning, managing, and controlling the solar energy system production. To overcome the gaps related to the intermittence and to the absence of SRC data, an accurate predictive model needs to be established. The main goal of the present work is to develop for solar system engineers and grid operators a precise predictive approach based on multi-target learning algorithm to forecast the hourly SRC measurements that is related to the city of Marrakesh (latitude 31°37′N, longitude 08°01′W, elevation 466m), Morocco, received by different inclined solar panels’ surfaces. For this purpose, eight training algorithms (Resilient back Propagation (Rp), One step secant (OSS), Levenberg-Marquardt (LM) Algorithm, Fletcher-Reeves algorithm (Cgf), Polak-Ribiere algorithm (Cgp), Powell-Beale algorithm (Cgb), gradient descent (Gd) algorithm and scaled conjugate gradient algorithm (Scg)) are tested to optimize the developed approach’s parameters. The forecasting results were performed based on the angle of inclination desired by the operator and some accessible meteorological measurements that are recorded at each hour, comprising time variables. The achieved performance demonstrates the stability and the accuracy of the established approach to estimate the hourly SRC time series compared to several recent literature studies.

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“…Numerical Weather Prediction (NWP) models are computationally expensive for the forecasting resolution necessary in these applications [25,26,27,28,29,30]. GSI forecasting models which include ground weather features from meso-scale meteorology have problems of collinearity [31]. Cloud information extracted from geostationary satellite images improved the performance of solar irradiance forecasting with respect to NWP models [32,33].…”

Section: Introductionmentioning

confidence: 99%

Detection of Clouds in Multiple Wind Velocity Fields using Ground-based Infrared Sky Images

Terrén-Serrano¹,

Martínez‐Ramón²

2021

Preprint

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Horizontal atmospheric wind shear causes wind velocity fields to have different directions and speeds. In images of clouds acquired using ground-based sky imagers, clouds may be moving in different wind layers. To increase the performance of an intra-hour global solar irradiance forecasting algorithm, it is important to detect multiple layers of clouds. The information provided by a solar forecasting algorithm is necessary to optimize and schedule the solar generation resources and storage devices in a smart grid. This investigation studies the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images. The images are acquired using an innovative infrared sky imager mounted on a solar tracker. Different mixture models are used to infer the distribution of the cloud features. The optimal decision criterion to find the number of clusters in the mixture models is analyzed and compared between different Bayesian metrics and a sequential hidden Markov model. The motion vectors are computed using a weighted implementation of the Lucas-Kanade algorithm. The correlations between the cloud velocity vectors and temperatures are analyzed to find the method that leads to the most accurate results. We have found that the sequential hidden Markov model outperformed the detection accuracy of the Bayesian metrics.

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Evaluation of dimensionality reduction methods applied to numerical weather models for solar radiation forecasting

Cited by 44 publications

References 57 publications

Girasol, a sky imaging and global solar irradiance dataset

Girasol, a sky imaging and global solar irradiance dataset

Multi-Target Learning Algorithm for Solar Radiation Components Forecasting Based on the Desired Tilt Angle of a Solar Energy System

Detection of Clouds in Multiple Wind Velocity Fields using Ground-based Infrared Sky Images

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