Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset

Moncada, Ariana; Richardson, W. B.; Vega-Avila, Rolando

doi:10.3390/en11081988

Cited by 47 publications

(17 citation statements)

References 22 publications

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“…Common techniques used for solar forecasting are Support Vector Regression (SVR) [13], Artificial Neural Networks (ANN) [14][15][16], hybrid approaches of ANN and SVR [17][18][19] or Vector Autoregression (VAR) [20].…”

Section: Introductionmentioning

confidence: 99%

Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production

Huertas‐Tato

Brito

2018

Energies

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Solar energy forecasting is an active research problem and a key issue to increase the competitiveness of solar power plants in the energy market. However, using meteorological, production, or irradiance data from the past is not enough to produce accurate forecasts. This article aims to integrate a prediction algorithm (Smart Persistence), irradiance, and past production data, using a state-of-the-art machine learning technique (Random Forests). Three years of data from six solar PV modules at Faro (Portugal) are analyzed. A set of features that combines past data, predictions, averages, and variances is proposed for training and validation. The experimental results show that using Smart Persistence as a Machine Learning input greatly improves the accuracy of short-term forecasts, achieving an NRMSE of 0.25 on the best panels at short horizons and 0.33 on a 6 h horizon.

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Section: Introductionmentioning

confidence: 99%

Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production

Huertas‐Tato

Brito

2018

Energies

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“…Details of our research on machine learning to predict solar irradiance are described in the paper [9]. Our intent here is to provide the reader with a concise summary of that research.…”

Section: Resultsmentioning

confidence: 99%

“…At some point however, statistics suggests diminishing returns. In addition to detailed descriptions of ML models and software our article [9] presents another case study. It uses only one moderately cloudy day (17 October 2015) of observations and runs the four ML models with 8 × 8, 32 × 32, and 64 × 64-pixel sub-images.…”

Section: Cloudy Versus Clear Sky Daysmentioning

confidence: 99%

“…It serves as a case study in managing research in a joint theatre of operations and integrating the efforts of researchers and engineers who came from very different university and industrial cultures. Details of our research and technology development have been presented in journal articles [8,9], conference proceedings [10][11][12][13], and technical reports [14].…”

Section: Introductionmentioning

confidence: 99%

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Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain

et al. 2019

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Increasing photovoltaic (PV) generation in the world’s power grid necessitates accurate solar irradiance forecasts to ensure grid stability and reliability. The University of Texas at San Antonio (UTSA) SkyImager was designed as a low cost, edge computing, all-sky imager that provides intra-hour irradiance forecasts. The SkyImager utilizes a single board computer and high-resolution camera with a fisheye lens housed in an all-weather enclosure. General Purpose IO pins allow external sensors to be connected, a unique aspect is the use of only open source software. Code for the SkyImager is written in Python and calls libraries such as OpenCV, Scikit-Learn, SQLite, and Mosquito. The SkyImager was first deployed in 2015 at the National Renewable Energy Laboratory (NREL) as part of the DOE INTEGRATE project. This effort aggregated renewable resources and loads into microgrids which were then controlled by an Energy Management System using the OpenFMB Reference Architecture. In 2016 a second SkyImager was installed at the CPS Energy microgrid at Joint Base San Antonio. As part of a collaborative effort between CPS Energy, UT San Antonio, ENDESA, and Universidad de La Laguna, two SkyImagers have also been deployed in the Canary Islands that utilize stereoscopic images to determine cloud heights. Deployments at three geographically diverse locations not only provided large amounts of image data, but also operational experience under very different climatic conditions. This resulted in improvements/additions to the original design: weatherproofing techniques, environmental sensors, maintenance schedules, optimal deployment locations, OpenFMB protocols, and offloading data to the cloud. Originally, optical flow followed by ray-tracing was used to predict cumulus cloud shadows. The latter problem is ill-posed and was replaced by a machine learning strategy with impressive results. R2 values for the multi-layer perceptron of 0.95 for 5 moderately cloudy days and 1.00 for 5 clear sky days validate using images to determine irradiance. The SkyImager in a distributed environment with cloud-computing will be an integral part of the command and control for today’s SmartGrid and Internet of Things.

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“…Solar radiation is a primary factor affecting power output. Some studies are ongoing with the goal of estimating solar radiation to predict future power output [30][31][32]. There are also studies on power output estimation based on ambient temperature, wind velocity, and incident light [33][34][35].…”

Section: Solar Power Estimation and Inverter Efficiency Analysismentioning

confidence: 99%

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Park¹,

Hong²,

Lim³

et al. 2020

Processes

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The photovoltaic (PV) industry is an important part of the renewable energy industry. With the growing use of PV systems, interest in their operation and maintenance (O&M) is increasing. In this regard, analyses of power generation efficiency and inverter efficiency are very important. The first step in efficiency analysis is solar power estimation based on environment sensor data. In this study, solar power was estimated using a univariate linear regression model. The estimated solar power data were cross-validated with the actual solar power data obtained from the inverter. The results provide information on the power generation efficiency of the inverter. The linear estimation model developed in this study was validated using a single PV system. It is possible to apply the coefficients presented in this study to other PV systems, even though the nature and error rates of the collected data may vary depending on the inverter manufacturer. To apply the proposed model to PV systems with different power generation capacities, reconstructing the model according to the power generation capacity is necessary.

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Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset

Cited by 47 publications

References 22 publications

Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production

Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production

Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

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