Investigation of energy demand data correlation to significant variables during movement control order

Saharani, Sharizad (2021) Investigation of energy demand data correlation to significant variables during movement control order. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

The worlds currently face a significant impact from the covid-19 pandemic, which also influences energy consumption. This study investigates the substantial connection of the classified data between energy consumption, cooling degree days, average temperature, and covid-19 cases information using mathematical and neural network approaches, which are regression analysis and self-organizing maps. It is well established that various data mining methods have revamped the classification process of data analytics. Specifically, this study investigates the correlation between the collected variables using regression analysis and selecting the best-matching unit under the normalization method using self-organizing maps. The self-organizing maps become better when the datasets have variations; the result denotes that this method produced high mapping quality based on the map size and normalization method. The data crossing connection is indicated by using the regression analysis method and the classified data results during the movement control order are validated in self-organizing maps to achieve the study objective. By performing these methods, this study established that the correlation between the energy demand towards cooling degree days, average temperature, and covid-19 cases is very weak and the verification of ‘logistic’ and ‘range’ normalization method produced the best classification result.

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