Analysis of Spatial Smoothing Effect of Short-term Fluctuation of Global Solar Radiation based on the Data Including Quantization Error

2011 ◽  
Vol 131 (5) ◽  
pp. 413-420 ◽  
Author(s):  
Yosuke Ue ◽  
Ryoichi Hara ◽  
Hiroyuki Kita ◽  
Yutaka Saito ◽  
Katsuyuki Takitani ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Quantization Error ◽  
Smoothing Effect ◽  
Spatial Smoothing ◽  
Short Term ◽  
Term Fluctuation

Analysis of spatial smoothing effect of short-term fluctuation of global solar radiation based on data including quantization error

2012 ◽  
Vol 180 (3) ◽  
pp. 55-63
Author(s):  
Yosuke Ue ◽  
Ryoichi Hara ◽  
Hiroyuki Kita ◽  
Yutaka Saito ◽  
Katsuyuki Takitani ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Quantization Error ◽  
Smoothing Effect ◽  
Spatial Smoothing ◽  
Short Term ◽  
Term Fluctuation

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 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 A Hybrid LSTM-Based Genetic Programming Approach for Short-Term Prediction of Global Solar Radiation Using Weather Data

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1187
Author(s):  
Rami Al-Hajj ◽  
Ali Assi ◽  
Mohamad Fouad ◽  
Emad Mabrouk
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Genetic Programming ◽  
Hybrid Model ◽  
Global Solar Radiation ◽  
Series Data ◽  
Programming Approach ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance's consistency. An accurate global solar radiation (GSR) prediction model can ensure overall system reliability and power generation scheduling. This article describes a nonlinear hybrid model based on Long Short-Term Memory (LSTM) models and the Genetic Programming technique for short-term prediction of global solar radiation. The LSTMs are Recurrent Neural Network (RNN) models that are successfully used to predict time-series data. We use these models as base predictors of GSR using weather and solar radiation (SR) data. Genetic programming (GP) is an evolutionary heuristic computing technique that enables automatic search for complex solution formulas. We use the GP in a post-processing stage to combine the LSTM models’ outputs to find the best prediction of the GSR. We have examined two versions of the GP in the proposed model: a standard version and a boosted version that incorporates a local search technique. We have shown an improvement in terms of performance provided by the proposed hybrid model. We have compared its performance to stacking techniques based on machine learning for combination. The results show that the suggested method provides significant improvement in terms of performance and consistency.

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 SHORT-TERM CLIMATOLOGY OF GLOBAL SOLAR RADIATION AT DAKSHIN GANGOTRI, ANTARCTICA

MAUSAM ◽  
2021 ◽  
Vol 44 (1) ◽  
pp. 111-114
Author(s):  
A. L. KOPPAR ◽  
P. L. KEDARE
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Short Term
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