OpenSolar: Promoting the openness and accessibility of diverse public solar datasets

Feng, Cong; Yang, Di; Hodge, Bri‐Mathias; Zhang, Jie

doi:10.1016/j.solener.2019.07.016

Cited by 34 publications

(5 citation statements)

References 62 publications

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“…This causal confusion might explain why models based on spatial perception with low skills in temporal modelling (CNN, LSTM, 3D-CNN and ConvLSTM) perform similarly to ECLIPSE on the RMSE metric. In addition, this could account for a CNN architecture's ability to score a FS as high as 25.47% on the 10 to 20-min ahead averaged GHI prediction solely from a single image of the sky [9] on another dataset [8]. Although temporal features are crucial for the key objective of vision-based solar energy forecasting, these are outweighed by spatial features when training and evaluating some models (CNN, LSTM, 3D-CNN, ConvLSTM) with a standard loss function (MSE, RMSE, MAE, etc.).…”

Section: Visualisation Of the Encoded Spatio-temporal Representationmentioning

confidence: 99%

ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy

Paletta,

Hu,

Arbod

et al. 2021

Preprint

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Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics, is based on the analysis of sequences of ground-taken sky images. Despite encouraging results, a recurrent limitation of current Deep Learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a systematic temporal lag and little ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatiotemporal neural network architecture that models cloud motion from sky images to predict both future segmented images and corresponding irradiance levels. We show that ECLIPSE anticipates critical events and considerably reduces temporal delay while generating visually realistic futures.

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Section: Visualisation Of the Encoded Spatio-temporal Representationmentioning

confidence: 99%

ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy

Paletta,

Hu,

Arbod

et al. 2021

Preprint

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“…The paper suggests the authorities of OGD be more adept concerning OGD in creating trust. [15] presents OpenSolar, a platform intended to improve the open use of solar datasets. OpenSolar facilitates improved research, innovation, and cooperation by offering a central platform for a variety of solar statistics.…”

Section: Literature Reviewmentioning

confidence: 99%

Solar Energy Data Analysis & Predictive Modeling: A Case Study on Open Data of Saudi Arabian Solar Energy

Kadampur

2024

Preprint

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Renewable energy especially insights into solar energy are crucial in the energy business cycle. In this paper, an impact study of meteorological variables on solar energy production is conducted. The paper discusses the pipeline of data analysis specific to the case study and exploits open-source machine-learning algorithms to build predictive models. RandomForestRegressor and XGBoosting regressors are used to build the predictive model. The application code is integrated into an analytic software service (Power BI) and tested on Solar energy Open Data of Saudi Arabia. The solar energy production is found to be high between 800-1000 barometric pressure locations. Global Horizontal Irradiation is found to increase when the atmospheric temperature is more than 34∘ C. The impact of relative humidity is that the produced energy is high at lower humidity points. The wind speed effect is that the produced energy is significant between 2 to 4 m/s and reaches its maximum at 2.4 m/s. The predictive models produce nearly real-time predictions with Random Forest with R²: 0.909. The paper provides Global Horizontal Irradiation estimation equations using meteorological coefficients generated by the trained regressor. The paper presents implementation methodologies and records various instances of test results. It highlights the efficacy of machine learning in predictive modeling for renewable energy applications.

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“…The measured meteorological variables of the SRRL, including the DNI, solar zenith angle, relative humidity, and air mass, are obtained with a 1 min sampling frequency, and the details of these variables are listed in Table 1. There are few negative values of DNI which are very close to zero and are corrected as 0 [27]. The total sky images used were RGB images obtained using the TSI (TSI-880), and the image resolution was 352 × 288 pixels.…”

Section: Data Collectionmentioning

confidence: 99%

Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory

Zhu

Guo

et al. 2021

Energies

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Photovoltaic power generation is highly valued and has developed rapidly throughout the world. However, the fluctuation of solar irradiance affects the stability of the photovoltaic power system and endangers the safety of the power grid. Therefore, ultra-short-term solar irradiance predictions are widely used to provide decision support for power dispatching systems. Although a great deal of research has been done, there is still room for improvement regarding the prediction accuracy of solar irradiance including global horizontal irradiance, direct normal irradiance and diffuse irradiance. This study took the direct normal irradiance (DNI) as prediction target and proposed a Siamese convolutional neural network-long short-term memory (SCNN-LSTM) model to predict the inter-hour DNI by combining the time-dependent spatial features of total sky images and historical meteorological observations. First, the features of total sky images were automatically extracted using a Siamese CNN to describe the cloud information. Next, the image features and meteorological observations were fused and then predicted the DNI in 10-min ahead using an LSTM. To verify the validity of the proposed SCNN-LSTM model, several experiments were carried out using two-year historical observation data provided by the National Renewable Energy Laboratory (NREL). The results show that the proposed method achieved nRMSE of 23.47% and forecast skill of 24.51% for the whole year of 2014, and it also did better than some published methods especially under clear sky and rainy days.

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OpenSolar: Promoting the openness and accessibility of diverse public solar datasets

Cited by 34 publications

References 62 publications

ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy

ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy

Solar Energy Data Analysis & Predictive Modeling: A Case Study on Open Data of Saudi Arabian Solar Energy

Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory

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OpenSolar: Promoting the openness and accessibility of diverse public solar datasets

Cited by 34 publications

References 62 publications

ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy

ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy

Solar Energy Data Analysis &amp; Predictive Modeling: A Case Study on Open Data of Saudi Arabian Solar Energy

Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory

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Solar Energy Data Analysis & Predictive Modeling: A Case Study on Open Data of Saudi Arabian Solar Energy