Software energy consumption prediction using software code metrics

In recent years, a significant amount of energy consumption of ICT products has resulted in environmental concerns. Growing demand for mobile devices, personal computers, and the widespread adaptation of cloud computing and data centers are the main drivers for the energy consumption of the ICT systems. Finding solutions for improving the energy efficiency of the systems has become an important objective for both industry and academia. In order to address the increase in ICT energy consumption, hardware technology, such as production of energy efficient processors, has been substantially improved. However, demand for energy is growing faster than improvements are being made on these energy-aware technologies. Therefore, in addition to hardware, software technologies must also be a focus of research attention. Although software does not consume energy by itself, its characteristics determine which hardware resources are made available and how much electrical energy is used. Current literature on the energy efficiency of software, highlights, in particular, a lack of measurements and models. In this dissertation, first, the relationship between software code properties and energy consumption is explored. Second, using static code metrics regression based energy consumption prediction models are investigated. Finally, the models performance are assessed using within product and cross-product energy consumption prediction approaches. For this purpose, a quantitative based retrospective cohort study was employed. As research methods, observational data collection, mining software repositories, and regression analysis were utilized. This research results show inconsistent relationships between energy consumption and code size and complexity attributes considering different types of software products. Such results provide a foundation of knowledge that static code attributes may give some insights but would not be the sole predictors of energy consumption of software products.

Modeling and Optimization for Piercing Efficiency and Energy Consumption Based on Mean Value Substaged KELM-PLS and GA Method

Mathematical Problems in Engineering ◽

10.1155/2014/132654 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Dong Xiao ◽

Jichun Wang

Keyword(s):

Energy Consumption ◽

Prediction Model ◽

Prediction Models ◽

Optimization Method ◽

Mean Value ◽

Genetic Optimization ◽

Simulation And Experiment ◽

Coupling Characteristics ◽

Piercing manufacture of seamless tubes is the process that pierces solid blank into tube hollow. Piercing efficiency and energy consumption are the important indexes in the production of seamless tubes. Piercing process has the multivariate, nonlinear, cross-coupling characteristics. The complex factors that affect efficiency and consumption make it difficult to establish the mechanism models for optimization. Based on the production process, this paper divides the piercing process into three parts and proposes the piercing efficiency and energy consumption prediction models based on mean value staged KELM-PLS method. On the basis of mean value staged KELM-PLS prediction model, the minimum piercing energy consumption and maximum piercing efficiency are calculated by genetic optimization algorithm. Simulation and experiment prove that the optimization method based on the piercing efficiency and energy consumption prediction model can obtain the optimal process parameters effectively and also provide reliable evidences for practical production.

A big data management system for energy consumption prediction models

Ninth International Conference on Digital Information Management (ICDIM 2014) ◽

10.1109/icdim.2014.6991404 ◽

2014 ◽

Cited By ~ 4

Author(s):

Wonjin Lee ◽

Byung-Won On ◽

Ingyu Lee ◽

Jungin Choi

Keyword(s):

Big Data ◽

Energy Consumption ◽

Data Management ◽

Management System ◽

Prediction Models ◽

Data Management System ◽

Energy consumption prediction models for the retail sector

CIBSE Technical Symposium 2011 ◽

10.3943/2011.0008 ◽

2011 ◽

Author(s):

Spyrou

Keyword(s):

Energy Consumption ◽

Prediction Models ◽

Retail Sector ◽

Evaluation and improvement of energy consumption prediction models using principal component analysis based feature reduction

Journal of Cleaner Production ◽

10.1016/j.jclepro.2020.123866 ◽

2021 ◽

Vol 279 ◽

pp. 123866 ◽

Cited By ~ 2

Author(s):

Tarannom Parhizkar ◽

Elham Rafieipour ◽

Aram Parhizkar

Keyword(s):

Principal Component Analysis ◽

Energy Consumption ◽

Prediction Models ◽

Principal Component ◽

Component Analysis ◽

Feature Reduction ◽

Optimizing Energy Consumption Prediction Models using Genetic Algorithms

The Journal of Contents Computing ◽

10.9728/jcc.2021.06.3.1.307 ◽

2021 ◽

Vol 3 (1) ◽

pp. 307-316

Author(s):

Seungmin Oh ◽

Yeonggwang Kim ◽

Muhammad Firoz Mridha

Keyword(s):

Genetic Algorithms ◽

Energy Consumption ◽

Prediction Models ◽

Energy Consumption Prediction Using Machine Learning; A Review

10.20944/preprints201903.0131.v1 ◽

2019 ◽

Cited By ~ 6

Author(s):

Amir Mosavi ◽

Abdullah Bahmani

Keyword(s):

Machine Learning ◽

Energy Consumption ◽

Prediction Models ◽

Relevant Literature ◽

Ensemble Prediction ◽

The Novel ◽

General Application ◽

Energy Type ◽

Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning models used in the general application of energy consumption. Through a novel search and taxonomy the most relevant literature in the field are classified according to the ML modeling technique, energy type, perdition type, and the application area. A comprehensive review of the literature identifies the major ML methods, their application and a discussion on the evaluation of their effectiveness in energy consumption prediction. This paper further makes a conclusion on the trend and the effectiveness of the ML models. As the result, this research reports an outstanding rise in the accuracy and an ever increasing performance of the prediction technologies using the novel hybrid and ensemble prediction models.

Research on Sparsity of Output Synapses in Echo State Networks

Mathematical Problems in Engineering ◽

10.1155/2018/1984524 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12

Author(s):

Xiaohui Mu ◽

Lixiang Li ◽

Xiangyu He

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Predictive Performance ◽

Computationally Efficient ◽

Echo State Networks ◽

Consumption Energy ◽

Network Prediction ◽

Improved Model ◽

This paper presents an improved model of echo state networks (ESNs) and gives the definitions of energy consumption, energy efficiency, etc. We verify the existence of redundant output synaptic connections by numerical simulations. We investigate the relationships among energy consumption, prediction step, and the sparsity of ESN. At the same time, the energy efficiency and the prediction steps are found to present the same variation trend when silencing different synapses. Thus, we propose a computationally efficient method to locate redundant output synapses based on energy efficiency of ESN. We find that the neuron states of redundant synapses can be linearly represented by the states of other neurons. We investigate the contributions of redundant and core output synapses to the performance of network prediction. For the prediction task of chaotic time series, the predictive performance of ESN is improved about hundreds of steps by silencing redundant synapses.

A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building

Energies ◽

10.3390/en9010057 ◽

2016 ◽

Vol 9 (1) ◽

pp. 57 ◽

Cited By ~ 45

Author(s):

Hamid Khosravani ◽

María Castilla ◽

Manuel Berenguel ◽

Antonio Ruano ◽

Pedro Ferreira

Keyword(s):

Neural Networks ◽

Energy Consumption ◽

Prediction Models ◽

Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

Sustainability ◽

10.3390/su12010109 ◽

2019 ◽

Vol 12 (1) ◽

pp. 109 ◽

Cited By ~ 5

Author(s):

Mansu Kim ◽

Sungwon Jung ◽

Joo-won Kang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Analysis ◽

Energy Consumption ◽

Prediction Models ◽

Residential Buildings ◽

Residential Energy ◽

Artificial Neural ◽

When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analysis techniques, it is now possible to analyze user information using big data techniques. Here, we endeavored to integrate user information with the physical characteristics of residential buildings to analyze how these elements impact energy consumption. Regression analysis was conducted to accurately identify the impact of each element on energy consumption. It was found that six elements were influential in all seasons: the number of exterior walls, housing direction, housing area, number of years occupied, number of household members, and the occupation of the household head. The elements that had an impact in each period were then derived. Based on the results of the regression analysis, input variables for the training of an artificial neural network (ANN) model were selected for each period, and residential energy consumption prediction models were implemented based on actual consumption. The elements identified as those affecting energy consumption, through regression analysis, can be used for implementing prediction models with advanced forms. This study is significant in that we derived influential elements from an integrative perspective.