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An Improved Spatiotemporal Time Series Modelling Procedure with Application to Forecasting of Solar Radiation

Indian Agricultural Research Journals

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Field Value
 
Title An Improved Spatiotemporal Time Series Modelling Procedure with Application to Forecasting of Solar Radiation
 
Creator Kumar, Ravi Ranjan
Sarkar, Kader Ali
Dhakre, Digvijaya Singh
Bhattacharya, Debasis
 
Subject Solar radiation; STARMA; ANN; Hybrid model; Spatial weight matrix; Forecasting.
 
Description The demand for energy and associated services to meet sustainable agricultural and economic growth and improve human health and lifestyle is increasing day by day. Hence, there is a need for systematic and scientific prediction of solar and other renewable sources of energy to meet these requirements. The main purpose of this study is to propose a hybrid Space-Time Autoregressive Moving Average Artificial Neural Network (STARMA-ANN) model for the precise and accurate forecasting of solar radiation for better planning and policy making. This approach has been implemented at seven geographical locations of Bihar in India. Spatial weight matrices have been used to describe all seven geographical locations and incorporated into the STARMA model to reflect the spatial and temporal correlation. To deal with nonlinear dynamics in the spatiotemporal data, ANN technique has been applied on residuals of the fitted STARMA model. The results have demonstrated that the proposed hybrid model performs better prediction accuracy than using conventional STARMA model, especially for spatiotemporal data with nonlinear characteristics of solar radiation.
 
Publisher Indian Society of Agricultural Statistics (ISAS)
 
Date 2024-09-10
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://epubs.icar.org.in/index.php/JISAS/article/view/155905
10.56093/jisas.v78i2.6
 
Source Journal of the Indian Society of Agricultural Statistics; Vol. 78 No. 2 (2024); 125-133
0019-6363
 
Language eng
 
Relation https://epubs.icar.org.in/index.php/JISAS/article/view/155905/55628
 
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