Performance of Two Variable Machine Learning Models to Forecast Monthly Mean Diffuse Solar Radiation across India under Various Climate Zones

Abstract

For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India’s climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates.

Diurnal Evolution and Estimates of Hourly Diffuse Radiation Based on Horizontal Global Radiation, in Cerrado-Amazon Transition, Brazil

“…However, there is not good method for computing the diffuse radiation under a cloudy sky. Regarding this topic, many researchers have recently carried out several studies with empirical formulas based on different parameters for different regions of the world [3,[25][26][27], new estimation methodologies such as machine learning models and hybrids models [28][29][30][31][32], and remote sensing applications [33,34]. This work aimed to analyze the seasonality and propose statistical models for estimating the hourly diffuse radiation incident on the horizontal in the Cerrado-Amazon transition, Mato Grosso state, Brazil.…”

“…However, there is not good method for computing the diffuse radiation under a cloudy sky. Regarding this topic, many researchers have recently carried out several studies with empirical formulas based on different parameters for different regions of the world [3,[25][26][27], new estimation methodologies such as machine learning models and hybrids models [28][29][30][31][32], and remote sensing applications [33,34].…”

Numerical Analysis of the Effect of Nanoparticles Size and Shape on the Efficiency of a Micro Heatsink

“…Heat sinks (HSs) are responsible for cooling electronic parts, which heat up considerably when operating [ 1 ]. They are used to cool devices such as LEDs [ 2 , 3 , 4 , 5 ], solar cells [ 6 , 7 , 8 , 9 , 10 , 11 ], CPUs [ 12 , 13 , 14 ], etc. Many electronic devices that need cooling have very small or micro-scale dimensions [ 15 , 16 , 17 ].…”