Analisis Heatmap Korelasi dan Scatterplot untuk Mengidentifikasi Faktor-Faktor yang Mempengaruhi Pelabelan AC efisiensi Energi

Desmarita Leni, Muchlisinalahuddin ., Maimuzar ., Haris ., Hendra .

Abstract


This research aims to evaluate the factors that affect AC energy efficiency using statistical analysis methods. The data used is energy-efficient AC labeling data obtained from the Directorate General of New, Renewable, and Energy Conservation Energy (EBETKE) database. Violin plots are used to see the distribution of the data, a correlation heatmap is used to display the level of correlation between variables, and scatterplots and R-squared values are used to visualize linear relationships and measure the strength of the relationship. The results of the study show that efficiency has a very strong positive correlation with rating, with a correlation coefficient of 0.75, while it has a weak negative correlation with other variables. The R-squared value obtained for the linear relationship between efficiency and rating is 0.56, which indicates that 56% of the variation in efficiency can be explained by the variation in rating. This result shows that rating is a very influential factor on AC energy efficiency.


Keywords


Correlation heatmap, scatterplot, AC labeling, energy efficiency

References


IEA, iea, https://www.iea.org/reports/the-future-of-cooling-in-southeast-asia

Ebetke, https://simebtke.esdm.go.id/sinergi/skem-label/konsumen/pengondisi-udara-ac

Gu, zuguang. complex heatmap visualization. imeta, 2022, 1.3: e43.

Köpp, cornelius; von mettenheim, hans-jörg; breitner, michael h. decision analytics with heatmap visualization for multi-step ensemble data. business & information systems engineering, 2014, 6.3: 131-140.

Yarbrough, i., et al. visualizing building energy demand for building peak energy analysis. energy and buildings, 2015, 91: 10-15.

Ahn, ki uhn, et al. big-data analysis on energy consumption of office buildings in seoul, korea. in: proceedings of the 15th ibpsa conference. 2017. p. 1540-1547.

Peng, Jinglin, et al. dataprep. eda: task-centric exploratory data analysis for statistical modeling in python. in: proceedings of the 2021 international conference on management of data. 2021. p. 2271-2280.

Kenny, martin; schoen, ingmar. violin superplots: visualizing replicate heterogeneity in large data sets. molecular biology of the cell, 2021, 32.15: 1333-1334.

Adler, jeremy; parmryd, ingela. quantifying colocalization by correlation: the pearson correlation coefficient is superior to the mander's overlap coefficient. cytometry part a, 2010, 77.8: 733-742.

Kasuya, eiiti. on the use of r and r squared in correlation and regression. hoboken, usa: john wiley & sons, inc., 2019.




DOI: https://doi.org/10.30596/rmme.v6i1.13133

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