scholarly journals Energy Consumption Prediction Using Machine Learning; A Review

2019 ◽  
Author(s):  
Amir Mosavi ◽  
Abdullah Bahmani
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Relevant Literature ◽  
Ensemble Prediction ◽  
The Novel ◽  
General Application ◽  
Energy Consumption Prediction ◽  
Energy Type ◽  
Consumption Prediction

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.

scholarly journals Research on Energy Consumption Prediction Based on Machine Learning

2021 ◽  
Vol 791 (1) ◽  
pp. 012100
Author(s):  
Ren Liu ◽  
Zhonghang Wang ◽  
Haihong Chen ◽  
Jie Yang
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

scholarly journals Software energy consumption prediction using software code metrics

2021 ◽  
Author(s):  
Sedef Akinli Koçak
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Prediction Models ◽  
Code Size ◽  
Research Attention ◽  
Software Products ◽  
Code Metrics ◽  
Software Code ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

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.

scholarly journals Machine learning for energy consumption prediction and scheduling in smart buildings

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Safae Bourhnane ◽  
Mohamed Riduan Abid ◽  
Rachid Lghoul ◽  
Khalid Zine-Dine ◽  
Najib Elkamoun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Smart Buildings ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

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

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
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 ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

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.

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