Effects of air pollution for estimating global solar radiation in India

Abstract

Solar energy is the primary resource for all biological, chemical and physical processes. The amount of global solar radiation is an important parameter for solar energy applications. It is common to estimate a monthly average of daily global solar radiation using different regression models. These models in turn exploit the correlation between solar radiation and various atmospheric factors. These factors are commonly derived from meteorological, geographical and climatological data that are readily available for majority of weather stations across the world. In this paper, a novel regression model that can predict location-independent daily global solar radiation is presented. The proposed exponential quadratic model captures the correlation between measured global solar radiation values, sunshine hour and Air Pollution Index for Indian cities. In addition to this, an extended study of several other regression models (e.g. linear, quadratic, exp.-linear and exp.-quadratic) is also presented. This analysis with real data from Indian cities suggests that air pollution is a more significant factor than location when predicting solar radiation. Finally, the model parameters (regression coefficients) for each model are listed out. Additionally, the generalised model equation for the best performing model is also presented.

Comprehensive review on empirical models to estimate global solar radiation on horizontal surfaces

“…During last 10 years as the emissions of GHGs have increased drastically, it is hard to see blue sky in many cities across the globe. Suthar [32] has realized this aspect and included the air pollution index in their study to estimate GSR. The environmental parameters need to be included while estimating global solar radiation.…”

“…Kumar [31] has developed new correlation using ANN techniques. Suthar [32] proposed an exponential quadratic model incorporating sunshine hour and air pollution index to estimate GSR for Indian cities after considering other regression models e.g., linear, quadratic, exponential linear and exponential quadratic. They were the opinion that air pollution was major significant factor instead of location for predicting GSR.…”

The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car

“…The AQI is a function of the amount of carbon monoxide (CO), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), fine particulate matter (PM 2.5 ), respirable particulate matter (PM 10 ), and ozone (O 3 ) in the air [22]. It has been shown that the AQI level is a strong contributor to the GHI forecast accuracy of a specific location subjected to air pollution or haze [22][23][24][25]. The South African Air Quality Information Systems (SAAQIS) [26], in Pretoria South Africa, provided the following annual average AQI values of the four locations which include their immediate surroundings: Pretoria (92), Bloemfontein (32), Port Elizabeth (16), and Stellenbosch (44).…”

Using Artificial Neural Networks for Prediction of Global Solar Radiation in Tehran Considering Particulate Matter Air Pollution

“…Moreover, applying empirical relations and by considering the amount of solar radiation factors and air pollution index (API), Suthar et al have presented an exponential quadratic equation with Root-Mean-Square Error of 3.08 and Mean Absolute Percentage Error of 0.1342 for India [24].…”