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

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%.

Keywords

• Cognitive Internet of Things
• CIoT framework
• Linear regression
• Electrical energy efficiency