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 this paper is to investigate the effect of residence time distribution in extruders along with the incorporation of nutraceuticals on the final quality of…
The purpose of this paper is to investigate the effect of residence time distribution in extruders along with the incorporation of nutraceuticals on the final quality of the products with respect to several pivotal responses.
Corn–rice flour blend fortified with isolated nutraceutical concentrates at two (low and high) levels was extruded at barrel temperature (110°C), screw speed (260 rpm) and feed moisture (17 percent). Extrudates were collected at an interval of 24 s followed by analysis for radial expansion (RE), bulk density (BD), water absorption index (WAI), sensory score (SS), textural hardness, colorimetric values (L*, a* and b*) and color difference (E).
The entire data were fitted to zero- and first-order kinetic models. There was a gradual decrease in RE, SS and L* value, whereas an increase in BD, textural hardness and a* value of extrudates fortified with the three nutraceutical concentrates was observed with the successive time interval of 24 s along with a more pronounced effect on color difference (E) observed during the last stages of extrusion time. The zero-order kinetic model was well fitted for BD and a* value, whereas the first-order kinetic model showed better results for RE, WAI, SS, textural hardness, L* value, a* value and b* value of fortified extrudates.
Nutraceuticals like β-glucans, lignans and γ oryzanol exhibit numerous health-beneficial effects. This study analyzes the kinetics of changes in various responses of extrudates fortified with these nutraceutical concentrates during extrusion.