scholarly journals Characterising Seasonality of Solar Radiation and Solar Farm Output

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 471 ◽  
Author(s):  
John Boland
Keyword(s):  
Solar Radiation ◽  
Electricity Market ◽  
Global Radiation ◽  
International Energy Agency ◽  
Global Solar Radiation ◽  
Clearness Index ◽  
Short Term ◽  
Energy Agency ◽  
Clear Sky ◽  
Short Term Forecasting

With the recent rapid increase in the use of roof top photovoltaic solar systems worldwide, and also, more recently, the dramatic escalation in building grid connected solar farms, especially in Australia, the need for more accurate methods of very short-term forecasting has become a focus of research. The International Energy Agency Tasks 46 and 16 have brought together groups of experts to further this research. In Australia, the Australian Renewable Energy Agency is funding consortia to improve the five minute forecasting of solar farm output, as this is the time scale of the electricity market. The first step in forecasting of either solar radiation or output from solar farms requires the representation of the inherent seasonality. One can characterise the seasonality in climate variables by using either a multiplicative or additive modelling approach. The multiplicative approach with respect to solar radiation can be done by calculating the clearness index, or alternatively estimating the clear sky index. The clearness index is defined as the division of the global solar radiation by the extraterrestrial radiation, a quantity determined only via astronomical formulae. To form the clear sky index one divides the global radiation by a clear sky model. For additive de-seasoning, one subtracts some form of a mean function from the solar radiation. That function could be simply the long term average at the time steps involved, or more formally the addition of terms involving a basis of the function space. An appropriate way to perform this operation is by using a Fourier series set of basis functions. This article will show that for various reasons the additive approach is superior. Also, the differences between the representation for solar energy versus solar farm output will be demonstrated. Finally, there is a short description of the subsequent steps in short-term forecasting.

scholarly journals SolarisNet : A deep regression network for solar radiation prediction

MAUSAM ◽  
2021 ◽  
Vol 71 (3) ◽  
pp. 443-450
Author(s):  
DEY SUBHADIP ◽  
PRATIHER SAWON ◽  
MUKHERJEE CHANCHAL KUMAR ◽  
BANERJEE SAON
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Parametric Models ◽  
High Tech ◽  
Short Term ◽  
Effective Utilization ◽  
Clear Sky ◽  
Past Data ◽  
Weather Stations ◽  
Short Term Forecasting

Effective utilization of photovoltaic (PV) plants requires weather variability robust global solar radiation (GSR) forecasting models. Random weather turbulence phenomena coupled with assumptions of clear sky model as suggested by Hottel pose significant challenges to parametric &non-parametric models in GSR conversion rate estimation. Also, a decent GSR estimate requires costly high-tech radiometer and expert dependent instrument handling and measurements, which are subjective. As such, a computer aided monitoring (CAM) system to evaluate PV plant operation feasibility by employing smart grid past data analytics and deep learning is developed. Our algorithm, SolarisNet is a 6-layer deep neural network trained on data collected at two weather stations located near Kalyani metrological site, West Bengal, India. The daily GSR prediction performance using SolarisNet outperforms the existing state of art and its efficacy in inferring past GSR data insights to comprehend daily and seasonal GSR variability along with its competence for short term forecasting is discussed.

scholarly journals Kalman filter model, as a tool for short-term forecasting of solar potential: case of the Dakar site

2018 ◽  
Vol 57 ◽  
pp. 01004
Author(s):  
A. Mbaye ◽  
J. Ndong ◽  
M.L. NDiaye ◽  
M. Sylla ◽  
M.C. Aidara ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Solar Radiation ◽  
Mean Squared Error ◽  
Model Performance ◽  
Global Solar Radiation ◽  
Plant Production ◽  
Bias Error ◽  
Short Term ◽  
Filter Model ◽  
Short Term Forecasting

The prediction of solar potential is an important step toward the evaluation of PV plant production for the best energy planning. In this study, the discrete Kalman filter model was implemented for short-term solar resource forecasting one the Dakar site in Senegal. The model input parameters are constituted at a time t of the air temperature, the relative humidity and the global solar radiation. The expected output at time t+T is the global solar radiation. The model performance is evaluated with the square root of the normalized mean squared error (NRMSE), the absolute mean of the normalized error (NMAE), the average bias error (NMBE). The model Validation is carried out by means of the data measured within the Polytechnic Higher School of Dakar for one year. The simulation results following the 20 minute horizon show a good correlation between the prediction and the measurement with an NRMSE of 4.8%, an NMAE of 0.27% and an NMBE of 0.04%. This model could contribute to help photovoltaic based energy providers to better plan the production of solar photovoltaic plants in Sahelian environments.

scholarly journals Clouds over Hyytiälä, Finland: an algorithm to classify clouds based on solar radiation and cloud base height measurements

2020 ◽  
Vol 13 (10) ◽  
pp. 5595-5619
Author(s):  
Ilona Ylivinkka ◽  
Santeri Kaupinmäki ◽  
Meri Virman ◽  
Maija Peltola ◽  
Ditte Taipale ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Global Radiation ◽  
Cloud Base ◽  
Aerosol Formation ◽  
Clearness Index ◽  
Detection Range ◽  
Clear Sky ◽  
Overall Performance ◽  
Sky Conditions ◽  
Cloud Base Height

Abstract. We developed a simple algorithm to classify clouds based on global radiation and cloud base height measured by pyranometer and ceilometer, respectively. We separated clouds into seven different classes (stratus, stratocumulus, cumulus, nimbostratus, altocumulus + altostratus, cirrus + cirrocumulus + cirrostratus and clear sky + cirrus). We also included classes for cumulus and cirrus clouds causing global radiation enhancement, and we classified multilayered clouds, when captured by the ceilometer, based on their height and characteristics (transmittance, patchiness and uniformity). The overall performance of the algorithm was nearly 70 % when compared with classification by an observer using total-sky images. The performance was best for clouds having well-distinguishable effects on solar radiation: nimbostratus clouds were classified correctly in 100 % of the cases. The worst performance corresponds to cirriform clouds (50 %). Although the overall performance of the algorithm was good, it is likely to miss the occurrences of high and multilayered clouds. This is due to the technical limits of the instrumentation: the vertical detection range of the ceilometer and occultation of the laser pulse by the lowest cloud layer. We examined the use of clearness index, which is defined as a ratio between measured global radiation and modeled radiation at the top of the atmosphere, as an indicator of clear-sky conditions. Our results show that cumulus, altocumulus, altostratus and cirriform clouds can be present when the index indicates clear-sky conditions. Those conditions have previously been associated with enhanced aerosol formation under clear skies. This is an important finding especially in the case of low clouds coupled to the surface, which can influence aerosol population via aerosol–cloud interactions. Overall, caution is required when the clearness index is used in the analysis of processes affected by partitioning of radiation by clouds.

scholarly journals Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3517 ◽  
Author(s):  
Anh Ngoc-Lan Huynh ◽  
Ravinesh C. Deo ◽  
Duc-Anh An-Vo ◽  
Mumtaz Ali ◽  
Nawin Raj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Short Term Memory ◽  
Global Solar Radiation ◽  
Multiple Time ◽  
Short Term ◽  
Time Step ◽  
Term Memory ◽  
Multiple Time Step ◽  
Long Short Term Memory

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

scholarly journals Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Boluwaji M. Olomiyesan ◽  
Onyedi D. Oyedum
Keyword(s):  
Solar Radiation ◽  
Comparative Study ◽  
Global Radiation ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Radiation Data ◽  
Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

scholarly journals Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 43 ◽  
Author(s):  
Mesbaholdin Salami ◽  
Farzad Movahedi Sobhani ◽  
Mohammad Ghazizadeh
Keyword(s):  
Neural Networks ◽  
Electricity Market ◽  
Simulation Optimization ◽  
Supply And Demand ◽  
Electricity Supply ◽  
Wavelet Neural Networks ◽  
Short Term ◽  
Swarm Optimization ◽  
Forecasting Accuracy ◽  
Short Term Forecasting

The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section.

Solar Radiation Over the Caribbean Island of Puerto Rico

Solar Energy ◽  
2004 ◽  
Author(s):  
Ramiro L. Rivera ◽  
Karim Altaii
Keyword(s):  
Puerto Rico ◽  
Solar Radiation ◽  
Global Radiation ◽  
Global Solar Radiation ◽  
Time Frame ◽  
Daily Basis ◽  
Caribbean Island ◽  
Monthly Average ◽  
Energy Conversion System ◽  
The Caribbean

Solar radiation was measured and recorded on a 5-minute, hourly and daily basis at a number of sites on the Caribbean island of Puerto Rico (located from 18° to 18° 30’N latitude and from 65° 30’ to 67° 15’W longitude) over a 24 calendar month time frame. The global solar radiation was measured at four sites (namely: Aguadilla, Ponce, Gurabo, and San Juan). The global solar radiation data was measured by an Eppley Precision Spectral Pyranometer (model PSP) mounted on a horizontal surface. This pyranometer is sensitive to solar radiation in the range of 0.285 ≤ λ ≤ 2.8 μm wavelengths. Statistical analysis such as the daily average, monthly average hourly, monthly average daily, and annual average daily global radiation are presented in this paper. Despite its small size, a 13 percent variation in the global solar radiation has been observed within the island. Reasonable solar radiation values, for solar energy conversion system installation, seem to exist at and possibly around Aguadilla.

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