Parametric optimization of Solar Parabolic Collector using metaheuristic Optimization

Dutta, Pijush; Biswas, Supradip Kumar; Majumder, Madhurima

doi:10.36647/ciml/02.01.a004

Cited by 3 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent days the most challenging thing are the problems related to energy and mankind facing it in huge manner. For deficiency in the energy and electric power, there are various countries are under economic stress (Dutta, Biswas, et al, 2021). Amount of natural resources has decreasing continuously & rapidly due to their usage has increased in very recent times (Аndreychik, 2020).…”

Section: Introductionmentioning

confidence: 99%

Early & Accurate Forecasting of Mid Term Wind Energy Based on PCA Empowered Supervised Regression Model

Dutta¹,

Shaw²,

Das³

et al. 2021

CIML

View full text Add to dashboard Cite

In the Development stage, information about the environment should be accessed. This data is pre-processed with the help of Principal Component Analysis (PCA) to reduce the irrelevant attributes. After that MLA’s such as Decision Tree (DT), Random Forest (RF), KNN, Linear regression (LR) & multilayer Neural Network model (MLP-ANN) models are utilized in testing datasets to predict the wind energy. The Power which is determined necessities to check and separate from the first ability to refresh the framework until and except if the necessary dependability is assembled from learning. In the assessment stage, the prepared expectation framework is then utilized to anticipate the Power for the test samples. In this research four statistical performance indicators & training time used to identify the best-fitted model. In the analysis section, it is seen that PCA Based DT outperformed the others algorithm by means of MAE, MSE, RMSE, Regression & Training time. Keyword : Wind speed prediction, Machine Learning techniques, Regression Analysis, Performance metrics

show abstract

Section: Introductionmentioning

confidence: 99%