Spatial forecasting of solar radiation using ARIMA model

Shadab, Ahzam; Ahmad, Shamshad; Said, Salim

doi:10.1016/j.rsase.2020.100427

Cited by 49 publications

(30 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…On the basis of monthly solar insolation data during 1984–2017, Shadab et al. ( 2020 ) implemented a seasonal ARIMA model to forecast solar radiation in Delhi. The proposed seasonal ARIMA model was found to explain the maximum forecasted insolation value in May and the minimum in the months of January and December.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of monthly relative humidity in Delhi, India, using SARIMA and ANN models

Shad

Sharma

Singh

2022

Model. Earth Syst. Environ.

View full text Add to dashboard Cite

Relative humidity plays an important role in climate change and global warming, making it a research area of greater concern in recent decades. The present study attempted to implement seasonal autoregressive moving average (SARIMA) and artificial neural network (ANN) with multilayer perceptron (MLP) models to forecast the monthly relative humidity in Delhi, India during 2017–2025. The average monthly relative humidity data for the period 2000–2016 have been used to carry out the objectives of the proposed study. The forecast trend in relative humidity declines from 2017 to 2025. The accuracy of the models has been measured using root mean squared error (RMSE) and mean absolute error (MAE). The results showed that the SARIMA model provides the forecasted relative humidity with RMSE of 6.04 and MAE of 4.56. On the other hand, MLP model reported the forecasted relative humidity with RMSE of 4.65 and MAE of 3.42. This study concluded that the ANN model was more reliable for predicting relative humidity than SARIMA model. Supplementary Information The online version contains supplementary material available at 10.1007/s40808-022-01385-8.

show abstract

Section: Introductionmentioning

confidence: 99%

Forecasting of monthly relative humidity in Delhi, India, using SARIMA and ANN models

Shad

Sharma

Singh

2022

Model. Earth Syst. Environ.

View full text Add to dashboard Cite

show abstract

“…The authors of [115] proposed a new Repeated Wavelet Transform (WT) based Auto Regressive Integrated Moving Average (ARIMA) (WT-ARIMA) model to predict the wind speed on very short time intervals. Time series ARIMA modelling is used in [116] to predict monthly radiations of the sun utilising remote sensing data obtained from National Aeronautics and Space Administration (NASA)'s POWER (Prediction of Worldwide Energy Resources). In order to reduce wind speed prediction errors, a novel approach is proposed in [117] that applies a sparse Bayesian-based robust functional regression model.…”

Section: Wind and Solar Forecasting Methodsmentioning

confidence: 99%

Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System

et al. 2021

View full text Add to dashboard Cite

Renewable Energy Sources (RES) have drawn significant attention in the past years to make the transition towards low carbon emissions. On the one hand, the intermittent nature of RES, resulting in variable power generation, hinders their high-level penetration in the power system. On the other hand, RES can aid not only to supply much more eco-friendly energy but also it allows the power system to enhance its stability by ancillary service provision. This article reviews the challenges related to the most intermittent RES utilised in Belgium, that is, wind energy and solar energy. Additionally, wind speed and solar irradiance variations, which are the cause of wind and solar intermittency, are studied. Then, recent techniques to forecast their changes, and approaches to accommodate or mitigate their impacts on the power system, are discussed. Finally, the latest statistics and future situation of RES in the Belgian power system are evaluated.

show abstract

“…Accuracy of the model is calculated based on the co-efficient and training phase. The ARX model is demonstrated in the Equation 5below [34]:…”

Section: Implementation Of Arxmentioning

confidence: 99%

Long Term Solar Power Generation Prediction using Adaboost as a Hybrid of Linear and Non-linear Machine Learning Model

Babbar¹,

Lau²,

Thang³

2021

IJACSA

View full text Add to dashboard Cite

The usage of renewable energy sources has increased manifolds in terms of electric utilities. From other nonconventional sources, solar energy has been seen as a promising and convenient source used around the globe. In terms of meeting the daily requirements, solar energy has huge potential to fulfil the world's demand. However, firstly the characteristic of solar energy is very unpredictable and intermittent due to variation of weather. Secondly, the optimization and the planning of smart grid effect the operation of PV system. Thus, prediction on the long horizon is needed to address this problem. Nevertheless, long term forecasting of solar power generation is deliberated as a challenging problem. Therefore, this paper proposes a 10 day ahead solar power forecasting using combination of linear and non-linear machine learning models. At first, the outputs are generated from Recurrent Neural Network (RNN), Support Vector Machine (SVM) and Autoregressive with exogenous variable (ARX). Later on, these three outputs are combined and are made as a strong classifier with the Adaptive boost (Adaboost) algorithm. The simulations were conducted on the data obtained from real PV plant. By the experimental results and discussion, it was endogenously concluded that the combination of all techniques with the Adaboost have increased the performances and showing the high accuracy as compare to the individual machine learning models. The hybrid Adaboost shows %MAPE 8.88, which proven high accuracy. While on the other hand, for the individual technique, RNN shows 10.88, SVM reveals 11.78 and ARX gives 13.00 of percentage MAPE. The improvement proves that combination of techniques performs better than individual models and proclaims the high accuracy.

show abstract

Spatial forecasting of solar radiation using ARIMA model

Cited by 49 publications

References 16 publications

Forecasting of monthly relative humidity in Delhi, India, using SARIMA and ANN models

Forecasting of monthly relative humidity in Delhi, India, using SARIMA and ANN models

Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System

Long Term Solar Power Generation Prediction using Adaboost as a Hybrid of Linear and Non-linear Machine Learning Model

Contact Info

Product

Resources

About