Using Artificial Neural Network for VM Consolidation Approach to Enhance Energy Efficiency in Green Cloud

2018 ◽  
pp. 139-154
Author(s):  
Anjum Mohd Aslam ◽  
Mala Kalra
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Green Cloud ◽  
Vm Consolidation

scholarly journals CLASSIFICATION OF THE ENERGY AND EXERGY OF MICROWAVE DRYERS IN DRYING KIWI USING ARTIFICIAL NEURAL NETWORKS

2019 ◽  
pp. 29-44
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Artificial Neural Network ◽  
Energy Loss ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Drying Process ◽  
Percent Correct ◽  
Energy And Exergy ◽  
Artificial Neural

ABSTRACT This investigation uses the artificial neural network model to classify the energy and exergy of the kiwi drying process in a microwave dryer. In this experiment, classification was carried out separately for various pretreatments and microwave powers using three pretreatments (oven, ohmic, and control treatments) and microwave power values (360, 600, and 900W), and the artificial neural network model. Classification was done using 5 different input data groups. The first group included the overall data (energy efficiency, special energy loss, exergy efficiency, and exergy loss), while the second to fifth groups included the data on the exergy efficiency, special energy loss, energy efficiency and special exergy loss in the order mentioned, which served as the classification inputs. Considering the results, the best R and Percent Correct values for the oven (Percent Correct=90 – R=0.709) and ohmic (Percent Correct=83.33– R=0.846) pretreatments were obtained. The values of this parameters were also calculated for the control (Percent Correct=71.43 – R=0.843), the 360W power (Percent Correct=92.86 – R=0.9975), the 600W power (Percent Correct=100 – R=0.9124), and the 900W power (Percent Correct=100 – R=0.9685). The overall data was used in the classification phase. In addition, the maximum correctly detected data for the oven, ohmic, and pretreatment was 18 (20 items), 15 (18 items), and 5 (7 items), respectively. The maximum correctly detected data for the 360W power, 600W power, and 900W power levels was 13 (14 items), 15 (15 items), and 16 (16 items), respectively. In sum, the neural network using the overall data input displayed acceptable efficiency in classifying the energy and exergy data of the kiwi drying process in microwave dryers

scholarly journals Tool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region

2021 ◽  
Vol 312 ◽  
pp. 02016
Author(s):  
Domenico Palladino ◽  
Iole Nardi
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Artificial Neural Network ◽  
National Level ◽  
Support Tool ◽  
Umbria Region ◽  
Building Systems ◽  
Artificial Neural ◽  
Forecasting Analysis

In order to reduce the greenhouse gas emission and to improve the energy efficiency of buildings, European Member States have to plan medium-to-long term strategies as reliable as possible. In this context, the present work aims to discuss the potentiality of Artificial Neural Network (ANN) as a support tool for medium-to-long term forecasting analysis of energy efficiency strategies in Umbria Region (central Italy) chosen as case study. Parametric energy simulations of several archetypes buildings were carried out in compliance with the current Italian regulations by changing the form, thermal properties, boundary conditions, and technical building systems. An ANN able to forecast primary energy need was trained to forecast the energy need of building-stock of Umbria Region and to evaluate the effectiveness of several potential energy actions (such as thermal coat or technical building systems replacement) over the years. Results confirm the potential of use of ANN as a support tool in energy forecasting analysis for local Authorities. ANN is capable of forecasting different future scenarios allowing correctly planning energy actions to be implemented as well as their priority. The results open to several scenarios of interest, such as the application of the same approach at national level.

Hybrid response surface methodology–artificial neural network optimization of drying process of banana slices in a forced convective dryer

2017 ◽  
Vol 24 (4) ◽  
pp. 277-291 ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Fatemeh Karimi ◽  
Mahmoud Karimi ◽  
Valiullah Lotfi ◽  
Golmohammad Khoobbakht
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Drying Rate ◽  
Drying Process ◽  
Air Velocity ◽  
Drying Time ◽  
Artificial Neural

The aim of the study is to fit models for predicting surfaces using the response surface methodology and the artificial neural network to optimize for obtaining the maximum acceptability using desirability functions methodology in a hot air drying process of banana slices. The drying air temperature, air velocity, and drying time were chosen as independent factors and moisture content, drying rate, energy efficiency, and exergy efficiency were dependent variables or responses in the mentioned drying process. A rotatable central composite design as an adequate method was used to develop models for the responses in the response surface methodology. Moreover, isoresponse contour plots were useful to predict the results by performing only a limited set of experiments. The optimum operating conditions obtained from the artificial neural network models were moisture content 0.14 g/g, drying rate 1.03 g water/g h, energy efficiency 0.61, and exergy efficiency 0.91, when the air temperature, air velocity, and drying time values were equal to −0.42 (74.2 ℃), 1.00 (1.50 m/s), and −0.17 (2.50 h) in the coded units, respectively.

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