Analysing the geographical impact on short-term solar irradiance forecasting using univariate and multivariate time series models

ABSTRACT

Sustainable development is significantly hampered by air pollution brought on by greenhouse gas emissions. One of the primary contributors to air pollution is coal, which is used in thermal power plants. Substituting power plants from combustible sources to renewable energy sources can reduce air pollution to an extent. Solar energy is one of the most readily accessible renewable energy sources. High Global Horizontal Irradiance (GHI) is advantageous for deploying solar power plants, allowing maximum electricity production. However, the spatiotemporal factors affecting GHI are not analyzed, which can improve the forecasting accuracy. The proposed work analyzes the geographical impact of solar irradiation using statistical time-series models called ARIMA (Auto Regression Integrated Moving Average) and VAR (Vector Auto Regression) using four datasets from two different locations in India. The statistical time-series models can analyze the impact of the features and can show how the effect improves the performance of the multivariate model compared to the univariate model. The study aims to find the influence of the geographical factors of Global Horizontal Irradiance (GHI) based on the location chosen to build a solar power plant. The effectiveness of the univariate and multivariate models in forecasting solar irradiance is also discussed. The RMSE (root mean square error) analysis shows that the multivariate model VAR outperforms the univariate model in predicting solar irradiance. There is a 1–25% variance in the RMSE values between the ARIMA and VAR models at different locations and a 1–19% variance in MAE (Mean Absolute Error) value, while a 94–98% variance in MAPE (mean absolute percentage error) value. A deep analysis of factors that affect the precision of global horizontal irradiance (GHI) and the causal impact of variables is also analyzed using different variable impact tests. This variable selection is achieved by a feature selection algorithm called Maximum Relevance Minimum Redundancy (MRMR).

KEYWORDS:

• Sustainable development
• renewable energy
• solar irradiance
• ARIMA model
• VAR model
• time series data
• univariate model
• multivariate model
• MRMR feature selection algorithm

Acknowledgements

The authors thank the Rajagiri School of Engineering and Technology and APJ Abdul Kalam Technological University for their support in completing this research work.

Disclosure statement

The authors declare that they have no known competing financial, non-financial, or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Notes on contributors

Ninu G. Hari

Ninu G. Hari received B.Tech degree from Cochin University of Science and Technology and M.Tech degree from Karunya University. Currently, she is researching Sustainable development at APJ Abdul Kalam Technological University. Her areas of interest are Deep Learning and Time Series models.

Jisha G.

Dr Jisha G holds a PhD degree in Information and Communication Engineering with a specialisation in Heterogeneous Networks. She is an Associate Professor in the Department of Computer Science & Engineering, Rajagiri School of Engineering and Technology, Kochi, India. She is a recognised research guide under APJ Abdul Kalam Technological University, Kerala, India. She has around 20 years of teaching experience. She has supervised more than 20 master's students and also guided research students. She has a patent published under application No.: 202141042022 for the title “IoT Enabled Step to Ramp for Mobility Impaired”. She has had publications in reputed journals and presented papers at prestigious conferences. Her research areas are Wireless networks, Image Processing, Machine Learning and its applications.