Estimation Of Global Solar Radiation by Using ANN and ANFIS

Alparslan, Nuh Cevdet; Kayabaşı, Ahmet; Rusen, Selmin Ener

doi:10.1109/asyu48272.2019.8946448

Cited by 4 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using atmospheric data and surface solar radiation values, this study provides significant insights for the design and optimization of solar energy systems. [10] In addition to providing significant new information about the techniques employed for accurate solar radiation estimation, it highlights the improved performance of models based on Artificial Neural Networks (ANNs). The study addresses the main concerns and difficulties associated with Turkey's feed-in tariff system, as well as the financial implications of it.…”

Section: Literature Surveymentioning

confidence: 99%

Breakeven Estimation of Solar Energy: A Machine Learning and Time Series Analysis Approach

et al. 2024

IJFMR

View full text Add to dashboard Cite

This study focuses on determining the breakeven point for solar panel installations, utilizing a comprehensive dataset with daily information like sunrise, sunset, and temperature. Users input parameters such as panel area, location, current cost, and installation charges. Using advanced time series analysis, the system considers environmental factors, energy potential, and local prices to predict when cumulative income will surpass installation costs. This analysis is crucial for individuals and organizations assessing the financial viability of solar investments. By providing insights into profitability timelines, stakeholders can make informed decisions, promoting a sustainable transition to renewable energy. The study aligns with the broader goal of reducing reliance on non-renewable sources and fostering environmentally responsible practices.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Breakeven Estimation of Solar Energy: A Machine Learning and Time Series Analysis Approach

et al. 2024

IJFMR

View full text Add to dashboard Cite

show abstract

“…Pedro and Coimbra (2012) showed that the short‐term forecasting efficacy of Artificial Neural Networks is comparable with that of the models in Reikard (2009). Ranganai and Sigauke (2020) used long memory models, and their hybrid versions to forecast irradiance 24 hours ahead at three different sites in South Africa; see also Tsekouras and Koutsoyiannis (2014), Lauret et al (2017), Voyant et al (2017) and Alparslan et al (2019). Moreover, Lonij et al (2013), Filik et al (2017) and Li and Zhang (2020) forecasted power outputs using PV systems.…”

Section: Introductionmentioning

confidence: 99%

A space‐time model with temporal cyclostationarity for probabilistic forecasting and simulation of solar irradiance data

Das

Alshehri

Stenchikov

et al. 2023

Stat

View full text Add to dashboard Cite

To establish a more sustainable future, Saudi Arabia is striving to reduce its dependency on fossil fuels and promote renewable energies. Solar energy is a major resource in Saudi Arabia because of the country's geographical location with year‐round clear skies and plentiful sunlight. However, although solar energy is a clean and safe renewable energy resource, it can be quite unpredictable. Therefore, solar irradiance needs to be forecasted and simulated as accurately as possible on both regional and national scales in the Kingdom to assist in planning for reserve usage, switching sources and short‐term power purchases. Based on an hourly solar diffuse horizontal irradiance (DHI) dataset from 45 different Saudi Arabian solar monitoring stations, this study proposes a novel spatio‐temporal model. We identify the temporal dependency of DHI as cyclostationary and incorporate this key observation in the model. This feature helps yield accurate (i.e. significantly close to the ground truth and with narrow confidence bands) probabilistic forecasts and realistic simulations in near‐future time points and at new locations. Then, the proposed model is compared with a stationary model that fails to recognize the cyclostationarity structure in the data. The proposed model produces significantly better predictions and simulations than the stationary model. Further, we calculate the photovoltaic power outputs using the simulated samples from both the models and the original observations and then compare them. The simulated samples from the proposed model afford photovoltaic power output estimates that are significantly closer to the original observed data than those from the stationary model, and therefore, they can better assist photovoltaic power output operators in assessing solar energy production and operational pre‐planning to mitigate impacts by the uncertain nature of solar irradiance.

show abstract

“…Other models that have been employed in medium term to long term forecasting are Numerical Weather Prediction (NWP) model, machine learning (a branch of artificial intelligence) (see e.g. [10]; [11]) and quantile regression (QR) based models (see e.g. [12]) .…”

mentioning

confidence: 99%

Capturing Long-Range Dependence and Harmonic Phenomena in 24-Hour Solar Irradiance Forecasting: A Quantile Regression Robustification via Forecasts Combination Approach

Ranganai

Sigauke

2020

IEEE Access

View full text Add to dashboard Cite

The global horizontal irradiance data recorded at the earth's horizontal surface is a mixture of deterministic (extra-terrestrially) and stochastic components due to the ever-changing atmospheric conditions. The Box-Jenkins short memory stochastic models and their hybrid versions have been used successfully to forecast solar irradiance data. However, these models lack robustness and faulter for distant horizon forecasting such as more than 2 hours in the hourly case. Using a quantile regression model as both a robust benchmark and robustification model, we remedy this drawback by using long memory models and their hybrid versions in 24 hour ahead irradiance forecasting at three sites in South Africa (RVD, UPR and SUN). Results show that the inherent long memory is anti-persistent in all models but one that shows persistence. The forecasts from these long memory-based models are generally within the prediction interval of the benchmark model forecasts for two sites. Based on the RMSE, rRMSE, MAE, rMAE and the pinball loss function at the central quantile level and two extreme quantile levels, forecasts obtained through robustification via forecasts combinations with the quantile regression yield the best performance. The predictive ability of the best model before forecasts combination against the quantile regression model combined (robustified) model based Diebold-Mariano test rejected the null hypothesis of the same accuracy. As a follow up to distinguish which has a superior predictive accuracy "murphy diagrams" were used. For the RVD and UPR sites, the model based on forecast combination has the best predictive accuracy while the quantile regression based benchmark model has the best accuracy for the SUN data.

show abstract

Estimation Of Global Solar Radiation by Using ANN and ANFIS

Cited by 4 publications

References 26 publications

Breakeven Estimation of Solar Energy: A Machine Learning and Time Series Analysis Approach

Breakeven Estimation of Solar Energy: A Machine Learning and Time Series Analysis Approach

A space‐time model with temporal cyclostationarity for probabilistic forecasting and simulation of solar irradiance data

Capturing Long-Range Dependence and Harmonic Phenomena in 24-Hour Solar Irradiance Forecasting: A Quantile Regression Robustification via Forecasts Combination Approach

Contact Info

Product

Resources

About