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

Distributed PV power generation necessitates both intra-hour and day-ahead forecasting of solar irradiance. The UTSA SkyImager is an inexpensive all-sky imaging system built using a Raspberry Pi computer with camera. Reconfigurable for different operational environments, it has been deployed at the National Renewable Energy Laboratory (NREL), Joint Base San Antonio, and two locations in the Canary Islands. The original design used optical flow to extrapolate cloud positions, followed by ray-tracing to predict shadow locations on solar panels. The latter problem is mathematically ill-posed. This paper details an alternative strategy that uses artificial intelligence (AI) to forecast irradiance directly from an extracted subimage surrounding the sun. Several different AI models are compared including Deep Learning and Gradient Boosted Trees. Results and error metrics are presented for a total of 147 days of NREL data collected during the period from October 2015 to May 2016.

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Evaluation of Photovoltaic Power Generation by using Deep Learning in Solar Panels Installed in Buildings

Energies ◽

10.3390/en12183564 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3564 ◽

Cited By ~ 1

Author(s):

Wei

Keyword(s):

Neural Network ◽

Power Generation ◽

Deep Learning ◽

Power Output ◽

Solar Panel ◽

Second Phase ◽

Solar Panels ◽

Generation Efficiency ◽

Southern Taiwan ◽

Pv Power Generation

Southern Taiwan has excellent solar energy resources that remain largely unused. This study incorporated a measure that aids in providing simple and effective power generation efficiency assessments of solar panel brands in the planning stage of installing these panels on roofs. The proposed methodology can be applied to evaluate photovoltaic (PV) power generation panels installed on building rooftops in Southern Taiwan. In the first phase, this study selected panels of the BP3 series, including BP350, BP365, BP380, and BP3125, to assess their PV output efficiency. BP Solar is a manufacturer and installer of photovoltaic solar cells. This study first derived ideal PV power generation and then determined the suitable tilt angle for the PV panels leading to direct sunlight that could be acquired to increase power output by panels installed on building rooftops. The potential annual power outputs for these solar panels were calculated. Climate data of 2016 were used to estimate the annual solar power output of the BP3 series per unit area. The results indicated that BP380 was the most efficient model for power generation (183.5 KWh/m2-y), followed by BP3125 (182.2 KWh/m2-y); by contrast, BP350 was the least efficient (164.2 KWh/m2-y). In the second phase, to simulate meteorological uncertainty during hourly PV power generation, a surface solar radiation prediction model was developed. This study used a deep learning–based deep neural network (DNN) for predicting hourly irradiation. The simulation results of the DNN were compared with those of a backpropagation neural network (BPN) and a linear regression (LR) model. In the final phase, the panel of module BP3125 was used as an example and demonstrated the hourly PV power output prediction at different lead times on a solar panel. The results demonstrated that the proposed method is useful for evaluating the power generation efficiency of the solar panels.

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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

Applied Sciences ◽

10.3390/app9040684 ◽

2019 ◽

Vol 9 (4) ◽

pp. 684 ◽

Cited By ~ 3

Author(s):

Walter Richardson ◽

David Cañadillas ◽

Ariana Moncada ◽

Ricardo Guerrero-Lemus ◽

Les Shephard ◽

...

Keyword(s):

Canary Islands ◽

Solar Irradiance ◽

Learning Strategy ◽

San Antonio ◽

Low Cost ◽

Climatic Conditions ◽

Operational Experience ◽

Reference Architecture ◽

Original Design ◽

Sky Imager

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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Using Deep Learning to Identify Potential Roof Spaces for Solar Panels

2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS) ◽

10.1109/icspcs.2018.8631725 ◽

2018 ◽

Author(s):

Dorian House ◽

Margaret Lech ◽

Melissa Stolar

Keyword(s):

Deep Learning ◽

Solar Panels

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A deep learning model for intra-day forecasting of solar irradiance using satellite-based estimations in the vicinity of a PV power plant

Solar Energy ◽

10.1016/j.solener.2021.02.033 ◽

2021 ◽

Vol 218 ◽

pp. 652-660

Author(s):

Emilio Pérez ◽

Javier Pérez ◽

Jorge Segarra-Tamarit ◽

Hector Beltran

Keyword(s):

Deep Learning ◽

Power Plant ◽

Solar Irradiance ◽

Learning Model ◽

Deep Learning Model

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Synthetic polarization-sensitive optical coherence tomography by deep learning

npj Digital Medicine ◽

10.1038/s41746-021-00475-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Yi Sun ◽

Jianfeng Wang ◽

Jindou Shi ◽

Stephen A. Boppart

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Imaging System ◽

Imaging Modality ◽

Similarity Index ◽

Polarization Sensitivity ◽

Imaging Method ◽

Optical Coherence ◽

The Real ◽

Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

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A Flexible and Robust Deep Learning-Based System for Solar Irradiance Forecasting

IEEE Access ◽

10.1109/access.2021.3051839 ◽

2021 ◽

Vol 9 ◽

pp. 12348-12361

Author(s):

Ignacio-Iker Prado-Rujas ◽

Antonio Garcia-Dopico ◽

Emilio Serrano ◽

Maria S. Perez

Keyword(s):

Deep Learning ◽

Solar Irradiance

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Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Inventions ◽

10.3390/inventions6020042 ◽

2021 ◽

Vol 6 (2) ◽

pp. 42

Author(s):

Worasit Sangjan ◽

Arron H. Carter ◽

Michael O. Pumphrey ◽

Vadim Jitkov ◽

Sindhuja Sankaran

Keyword(s):

Real Time ◽

Imaging System ◽

Crop Growth ◽

Sensor System ◽

Plant Phenotyping ◽

Raspberry Pi ◽

The Internet ◽

Growth Stages ◽

Crop Breeding ◽

Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

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Hierarchical and Multi-resolution Neuron Network Computing for Complex Inverse Problems-An Approach Based on Brain-inspired Computing and Learning Method

Current Chinese Computer Science ◽

10.2174/2665997201666210329104604 ◽

2021 ◽

Vol 01 ◽

Author(s):

Mingyong Zhou

Keyword(s):

Deep Learning ◽

Inverse Problems ◽

Radar Imaging ◽

Network Computing ◽

Neuron Network ◽

Impedance Tomography ◽

General Complex ◽

Mathematical Algorithms ◽

Ill Posed ◽

Regular Operators

Background: Complex inverse problems such as Radar Imaging and CT/EIT imaging are well investigated in mathematical algorithms with various regularization methods. However it is difficult to obtain stable inverse solutions with fast convergence and high accuracy at the same time due to the ill-posed property and non-linear property. Objective: In this paper, we propose a hierarchical and multi-resolution scalable method from both algorithm perspective and hardware perspective to achieve fast and accurate solu-tions for inverse problems by taking radar and EIT imaging as examples. Method: We present an extension of discussion on neuromorphic computing as brain-inspired computing method and the learning/training algorithm to design a series of problem specific AI “brains” (with different memristive values) to solve a general complex ill-posed inverse problems that are traditionally solved by mathematical regular operators. We design a hierarchical and multi-resolution scalable method and an algorithm framework to train AI deep learning neuron network and map into the memristive circuit so that the memristive val-ues are optimally obtained. We propose FPGA as an emulation implementation for neuro-morphic circuit as well. Result: We compared the methodology between our approach and traditional regulariza-tion method. In particular we use Electrical Impedance Tomography (EIT) and Radar imaging as typical examples to compare how to design an AI deep learning neuron network architec-tures to solve inverse problems. Conclusion: With EIT imaging as a typical example, we show that any moderate complex inverse problem, as long as it can be described as combinational problem, AI deep learning neuron network is a practical alternative approach to try to solve the inverse problems with any given expected resolution accuracy, as long as the neuron network width is large enough and computational power is strong enough for all combination samples training purpose.

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Pendeteksian Septoria pada Tanaman Tomat dengan Metode Deep Learning berbasis Raspberry Pi

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i1.2831 ◽

2021 ◽

Vol 5 (1) ◽

pp. 107-113

Author(s):

Kahlil Muchtar ◽

Chairuman ◽

Yudha Nurdin ◽

Afdhal Afdhal

Keyword(s):

Deep Learning ◽

Leaf Spot ◽

Raspberry Pi ◽

Leaf Spot Disease ◽

Detection Accuracy ◽

Crop Failure ◽

Tomato Production ◽

Spot Disease ◽

Average Accuracy ◽

Septoria Leaf Spot

much needed to meet the needs of both industry and households. However, tomato plants still require serious handling in increasing the yields. Data from the Central Bureau of Statistics shows that the number of tomatoes produced is not in accordance with a large number of market demands, resulting from the decrease of tomato yields. One of the obstacles in increasing tomato production is that the crops are attacked by septoria leaf spot disease due to the fungus or the fungus Septoria Lycopersici Speg. Most farmers have limited knowledge of the early symptoms, which are not obvious, and also facing difficulty in detecting this disease earlier. The problem has been causing disadvantages such as crop failure or plant death. Based on this problem, a study will be conducted with the aim of designing a tool that can be used to detect septoria leaf spot disease based on deep learning using the Convolutional Neural Network (ConvNets or CNN) model, where an algorithm that resembles human nerves is one of the supervised learning and widely used for solving linear and non-linear problems. In addition, the researcher used the Raspberry Pi as a microcontroller and used the Intel Movidius Neural Computing Stick (NCS) which functions to speed up the computing process so that the detection process is easier because of its portable, fast and accurate nature. The average accuracy rate is 95.89% with detection accuracy between 84.22% to 100%.

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