A regression analysis into Nepali ICT's energy consumption and its implications

Regmi, Nischal; Pandey, Shailesh B.

doi:10.1109/skima.2015.7400034

Cited by 2 publications

(4 citation statements)

References 27 publications

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“…The 'logistic' normalization U-matrix shows that there are two groups are formed during MCO. The red-dotted box consists of days (6,7,12,13,19,20,21,28,29) labeled as (F, G, L, M, S, T, U, B1, C1), and the second group of data is in the green-dotted box consisting of the day (8,9,10,11) labeled as (H, I, J, K) are placed in a single, separated group, which means that the average data from these days have a connection or dataset is the same but separated with the others. U-matrix shows that every variable in the 'logistic' approach has a different type of topology grouped in black boxes.…”

Section: Verification Using Som Outputmentioning

confidence: 99%

“…In the previous study, regression analysis is widely used for forecasting analysis. For example, communication technology has used regression for energy demand forecasting [10]. However, the regression analysis method is considered first layer analysis due to direct calculation settings when dealing with the low connection between data sets or having a low R² value.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of energy demand correlation during pandemic using self-organizing map algorithm

Sulaima

Saharani

Ahmad

et al. 2022

IJ-AI

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The world faces a significant impact from the coronavirus disease 2019 (Covid-19) pandemic, which also influences energy consumption. This study investigates the substantial connection of the classified data between power consumption, cooling degree days, average temperature, and covid-19 cases information using mathematical and neural network approaches 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 selforganizing 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. Furthermore, the data crossing connection is indicated using the regression analysis method. Finally, 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. The verification has been made where the ‘logistic’ normalization method has produced the best classification result.

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Section: Verification Using Som Outputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of energy demand correlation during pandemic using self-organizing map algorithm

Sulaima

Saharani

Ahmad

et al. 2022

IJ-AI

View full text Add to dashboard Cite

show abstract

“…From the physical perspective, the development of ICT has a strong dependence on energy infrastructure (see, Van Heddeghem et al, 2014). Regmi and Pandey (2015) mention the Nepali ICT policies have neglected the energy-infrastructure nexus and warn that the energy consumption of existing Nepali ICT infrastructure has already become comparable to the transportation sector. Without any long term visions, the underdeveloped energy infrastructure will soon emerge as the foremost barrier to ICT development in the country (Regmi & Pandey, 2015).…”

Section: Affordability and Other Prerequisitesmentioning

confidence: 99%

“…Regmi and Pandey (2015) mention the Nepali ICT policies have neglected the energy-infrastructure nexus and warn that the energy consumption of existing Nepali ICT infrastructure has already become comparable to the transportation sector. Without any long term visions, the underdeveloped energy infrastructure will soon emerge as the foremost barrier to ICT development in the country (Regmi & Pandey, 2015). Pandey and Raj (2016) mention that the reason for Internet non-use is the lack of local evidences which could convince the non-users about the benefits of the Internet.…”

Section: Affordability and Other Prerequisitesmentioning

confidence: 99%

Expectations versus Reality: A Case of Internet in Nepal

Regmi

2017

E J Info Sys Dev Countries

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The relation between Internet and development rests on the preconditions of minimum access, quality, affordability and many more. Presenting the picture of access, quality, and affordability, we raise doubts that Nepali internet infrastructure in its present form has any contribution to economic development. The Nepali policy circle also has felt the necessity of proliferating Internet access and use. But we argue major targets of the Nepali ICT policies are detached from the reality and hence failure prone. The formulation of a robust ICT policy cannot bypass the analyses of actualities of the demand side. There is a dearth of detailed statistics of ICT access, use, and affordability in Nepal. We thus recommend the government should conduct a census of ICT access, use, and affordability that would help the formulation of more grounded policies.

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A regression analysis into Nepali ICT's energy consumption and its implications

Cited by 2 publications

References 27 publications

Investigation of energy demand correlation during pandemic using self-organizing map algorithm

Investigation of energy demand correlation during pandemic using self-organizing map algorithm

Expectations versus Reality: A Case of Internet in Nepal

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