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Cognitive internet of things-based framework for efficient consumption of electrical energy in public higher learning institutions

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Bibliographic Details
Title: Cognitive internet of things-based framework for efficient consumption of electrical energy in public higher learning institutions
Authors: Ellen A. Kalinga, Simon Bazila, Kwame Ibwe, Abdi T. Abdalla
Source: Journal of Electrical Systems and Information Technology, Vol 10, Iss 1, Pp 1-18 (2023)
Publisher Information: SpringerOpen, 2023.
Publication Year: 2023
Collection: LCC:Electrical engineering. Electronics. Nuclear engineering
LCC:Information technology
Subject Terms: Cognitive Internet of Things, CIoT framework, Linear regression, Electrical energy efficiency, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Information technology, T58.5-58.64
Description: Abstract Electric energy is widely used to power homes, businesses, industries, and Higher Learning Institutions. However, the behavioral trend of using electricity poses challenges in saving energy. Most HLIs electricity users do not switch-off electrical appliances such as lights, fans, and air conditioners when not in use, resulting in high electricity bills and a shorter equipment life span. The literature indicates that misuse of electrical power is more of a behavioral matter, which can be challenging to control. In such scenarios, technological intervention is needed to minimize human interaction. Therefore, this work developed a Cognitive Internet of Things (CIoT)-based framework for efficient consumption of electrical energy in HLIs. CIoT has been applied in the context of saving electrical energy. The proposed framework uses the Linear Regression model for training to monitor air conditioners, fans, and light bulbs. The model compared measured values with established thresholds to perform the necessary actions. Training results from the Linear Regression model show that the air conditioning model achieved an of 97.5%, a chi-square, R 2, value of 0.450, a standard error of 0.524, and a "t" value of − 4.638% accuracy. The model for fans scored 97.5% accuracy with a chi-square, R 2, of 0.314, a standard error of 8.58 × 10–11, and a "t" value of 5.229. On the other hand, the lighting model scored an accuracy of 97.5% with a chi-square, R 2, of 0.298, a standard error of 0.396, and a "t" value of 0.311. All scenarios for testing the model using real data were successfully achieved 100%.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2314-7172
Relation: https://doaj.org/toc/2314-7172
DOI: 10.1186/s43067-023-00079-0
Access URL: https://doaj.org/article/e6737d43b3104d5babd353c5f522611a
Accession Number: edsdoj.6737d43b3104d5babd353c5f522611a
Database: Directory of Open Access Journals
Description
Abstract:Abstract Electric energy is widely used to power homes, businesses, industries, and Higher Learning Institutions. However, the behavioral trend of using electricity poses challenges in saving energy. Most HLIs electricity users do not switch-off electrical appliances such as lights, fans, and air conditioners when not in use, resulting in high electricity bills and a shorter equipment life span. The literature indicates that misuse of electrical power is more of a behavioral matter, which can be challenging to control. In such scenarios, technological intervention is needed to minimize human interaction. Therefore, this work developed a Cognitive Internet of Things (CIoT)-based framework for efficient consumption of electrical energy in HLIs. CIoT has been applied in the context of saving electrical energy. The proposed framework uses the Linear Regression model for training to monitor air conditioners, fans, and light bulbs. The model compared measured values with established thresholds to perform the necessary actions. Training results from the Linear Regression model show that the air conditioning model achieved an of 97.5%, a chi-square, R 2, value of 0.450, a standard error of 0.524, and a "t" value of − 4.638% accuracy. The model for fans scored 97.5% accuracy with a chi-square, R 2, of 0.314, a standard error of 8.58 × 10–11, and a "t" value of 5.229. On the other hand, the lighting model scored an accuracy of 97.5% with a chi-square, R 2, of 0.298, a standard error of 0.396, and a "t" value of 0.311. All scenarios for testing the model using real data were successfully achieved 100%.
ISSN:23147172
DOI:10.1186/s43067-023-00079-0