Power management in households has become the periodic issue for electric suppliers and household occupants. The number of electronic appliances is increasing day by day…
Power management in households has become the periodic issue for electric suppliers and household occupants. The number of electronic appliances is increasing day by day in every home with upcoming technology. So, it is becoming difficult for the energy suppliers to predict the power consumption for households at the appliance level. Power consumption in households depends on various factors such as building types, demographics, weather conditions and behavioral aspect. An uncertainty related to the usage of appliances in homes makes the prediction of power difficult. Hence, there is a need to study the usage patterns of the households appliances for predicting the power effectively.
Principal component analysis was performed for dimensionality reduction and for finding the hidden patterns to provide data in clusters. Then, these clusters were further being integrated with climate variables such as temperature, visibility and humidity. Finally, power has been predicted according to climate using regression-based machine learning models.
Power prediction was done based on different climatic conditions for electronic appliances in the residential sector. Different machine learning algorithms were implemented, and the result was compared with the existing work.
This will benefit the society as a whole as it will help to reduce the power consumption and the electricity bills of the house. It will also be helpful in the reduction of the greenhouse gas emission.
The proposed work has been compared with the existing work to validate the current work. The work will be useful to energy suppliers as it will help them to predict the next day power supply to the households. It will be useful for the occupants of the households to complete their daily activities without any hindrance.
The purpose of the study is to examine the relationship between the application of smart electronic systems, firm characteristics and efficient energy consumption: a case…
The purpose of the study is to examine the relationship between the application of smart electronic systems, firm characteristics and efficient energy consumption: a case of public universities in Uganda.
The study adopted both quantitative and qualitative approach as well as descriptive cross-sectional survey design tantamounting to an experimental-observation approach. A sample of four public academic universities were explored using primary data. A semi-structured questionnaire together with an evaluation form and a tested experimental kit (from one of the leading electronics centres in Uganda) was used to examine the consumption rates of different electronic appliances of less than 30 Amps. Further, a Pearson product moment correlation (r) analysis was also used to determine the direction of a relationship among the variables together with a linear relationship (regression) to predict a linear association of one or more variables. Recommendations were also given.
Smart electronic systems make a significant determining factor to both firm characteristics (age, number of students, administrative staff and support staff) as well as efficient energy consumption. Nonetheless, there is no significant difference of efficient energy consumption as far as firm characteristics are concerned.
Results support the contributions of the theory of technology and acceptance model by affirming that a number of factors influence the usefulness and ease of use of the smart electronic systems, which in turn influence energy consumption.
Universities' management should endeavour to install smart electronic systems. But still, government should try to lower taxes on smart electronic systems and genuine agents should be named for easy and affordable access of the users, universities inclusive.
The study contributes towards a theoretical position by affirming the usefulness of technology acceptance model for efficient energy consumption in public universities.
The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2019-0083