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Energy-efficient scheduling on heterogeneous multi-core architectures
2012
Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design - ISLPED '12
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A regression model is developed to estimate the energy consumption on the real heterogeneous multi-core platform. ...
Our scheduling approach maps the program to the most appropriate core, based on program phases, through a combination of static analysis and runtime scheduling. ...
We greatly appreciate research gifts and equipment donations from Intel, and helpful discussions with Ganapati Srinivasa, Ravishankar Iyer, and Mishali Naik from Intel concerning the Intel QuickIA system ...
doi:10.1145/2333660.2333737
dblp:conf/islped/CongY12
fatcat:bssg7dbxkvdmtexcaj3f6yguhq
Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption
2020
Energies
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model based on the multi-headed attention with the convolutional recurrent neural network. ...
A specific window for several sensor signals can induce various features extracted to forecast the energy consumption by using a prediction model. ...
Effects of Multi-Headed Attention For a thorough comparison, in addition to the neural networks proposed in previous works for energy prediction, we implement four additional neural networks and compared ...
doi:10.3390/en13184722
fatcat:lctifsqpzbdjxegvs57yu6lqtm
Machine Learning Methods Applied to Building Energy Production and Consumption Prediction
2019
Irish Conference on Artificial Intelligence and Cognitive Science
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Therefore, the objective of this article is to compare the application of different ML methods, aiming to predict PV energy production and energy consumption for residential users. ...
Overall, all the algorithms applied achieved mean errors below 14%, but the Boosted Decision Tree overperformed, with mean errors of 2.68% and 10% for energy consumption and energy production prediction ...
The results from our tests show a mean error below 14% for PV production prediction and below 6% for energy consumption. ...
dblp:conf/aics/LissaPSBSK19
fatcat:t75nrjmuanhvbkmjfu6fj7qgg4
On the Energy Consumption Forecasting of Data Centers Based on Weather Conditions: Remote Sensing and Machine Learning Approach
[article]
2018
arXiv
pre-print
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A relation between the energy consumption and the weather conditions would indicate that weather forecast models could be used for predicting energy consumption of DCs. ...
Then, by using multi-variable linear regression process, we model this correlation between the energy consumption and the dominant weather conditions parameters in order to effectively forecast the energy ...
ACKNOWLEDGMENT Part of this work has been funded by the FIESTA-IoT project, with GA number: 643943. The REAL-DC testbed has been utilized for gathering the data that have been used in this work. ...
arXiv:1804.01754v2
fatcat:s63xemg5jreq5fuadgmrfanlju
Statistical Features Based Approach (SFBA) for Hourly Energy Consumption Prediction Using Neural Network
2017
International Journal of Information Technology and Computer Science
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In this paper, new statistical features based approach (SFBA) for hourly energy consumption prediction using Multi-Layer Perceptron is presented. ...
For hourly energy consumption prediction, a total of six weeks data of ten residential buildings has been used. ...
For the classification and prediction of energy consumption Multi-Layer Perceptron has been selected based on the strong prediction capability.
B. ...
doi:10.5815/ijitcs.2017.05.04
fatcat:4ckfbf5ujzgezbkxtjunsxfdzi
Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review
2021
SN Computer Science
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Prediction of energy consumption and price is crucial in formatting policies related to the global energy market, demand, and supply. ...
We have investigated certain research papers and given conclusion based on their researched and proposed model's prediction results and accuracies in respective areas. ...
Acknowledgements The authors are grateful to School of Petroleum ...
doi:10.1007/s42979-021-00698-2
fatcat:rq463iesk5hr5poq5qitx2ynlm
Various Electricity Load Forecasting Techniques with Pros and Cons
2020
International journal of recent technology and engineering
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The rapid growth of stored information in the demand forecasting, associated with data analysis provoked an utmost need for generating a powerful tool which must be capable of extracting hidden and vital ...
Based upon the rigorous survey, primary challenges involved in the current technologies and future goals are also discussed. ...
The demand of Energy is modeled as the task of the "Gross Domestic Product", import-export, Population and Data (1990 to 2008) was used for prediction. ...
doi:10.35940/ijrte.f6997.038620
fatcat:nhpc7ewxqrbajg7dqe4ebvm6ui
Multi-objective Optimisation of Electric Arc Furnace Using the Non-dominated Sorting Genetic Algorithm II
2021
IEEE Access
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Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective ...
both EAF and ladle furnaces but also simultaneously minimises the total scrap cost and EAF energy consumption per ton of scrap. ...
Figure 7 shows the regression line for one of the outputs of the EAF regression model, the energy consumption. ...
doi:10.1109/access.2021.3125519
fatcat:fou2hbrk3rfshfct7zxvdoviea
Prediction of Energy Consumption in Buildings Using Support Vector Machine
2021
Tehnički Vjesnik
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Thus, having a model for energy usage prediction is of crucial importance. Data for sixty real-built buildings were collected. ...
Using support vector machine, a model was developed for prediction of energy consumption. ...
Previous investigations for energy consumption of buildings are based on a variety of techniques and methods, such as: neural networks, linear regression, multi-linear regression, etc. ...
doi:10.17559/tv-20190822153751
fatcat:3dnfbrsy6vgk5fahpda6bx7vuu
An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities
2021
IEEE Access
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This paper addresses the estimation of household communities' overall energy usage and solar energy production, considering different prediction horizons. ...
To address these issues, we propose a predicting approach that first considers the highly influential factors and, second, benefits from an ensemble learning method where one Gradient Boosted Regression ...
Historical load variables refer to energy consumption and production of households at previous hours. ...
doi:10.1109/access.2021.3063066
fatcat:75smlkni6vbz7bel3ur6zborsy
Image Classification on IoT Edge Devices: Profiling and Modeling
[article]
2019
arXiv
pre-print
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1902.11119v1 fatcat:g7x5i572fjf4vgewzze6jlaiki
The performance as well as the trade offs for using linear regression, Gaussian process, and random forests are discussed and validated. ...
In addition, in order to provide a means of predicting the energy consumption of an edge device performing image classification, we investigate the usage of three machine learning algorithms using the ...
This project involves the development of a flood monitoring system where Linux-based wireless systems, which rely on solar or battery power, capture images for analysis using ML to classify and report ...
arXiv:1902.11119v2
fatcat:xgih3bemf5dxtajb4ryuypaayu
National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO2 Emissions in Central—Eastern European Countries by Using Machine Learning Algorithms and Panel Data Analysis
2021
Energies
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The findings emphasize that separate components of energy consumption affect carbon emissions and, therefore, a transition toward renewable sources for energy needs is desirable. ...
a high consumption of nonrenewable energy. ...
The in-depth analysis based on the MLR linear regression models gives us a detailed image on how carbon emissions evolve depending on energy-related variables and urbanization. ...
doi:10.3390/en14102775
fatcat:x2c6k77p6zgy7mmkfae35gx7wu
Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts
2019
Sustainability
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Several different Machine Learning (ML) methodologies have been tested for predicting the energy consumption/production based on the information of hydro-meteorological data. ...
Reliable predictions of the energy consumption and production is important information for the management and integration of renewable energy sources. ...
Acknowledgments: MeteoSwiss is greatly acknowledged for providing all used meteorological data and Swissgrid for making the energy consumption/production data publicly accessible. ...
doi:10.3390/su11123328
fatcat:ylrj2btm4rgwnkpunq7zsfo6xa
Generalised Regression Hypothesis Induction for Energy Consumption Forecasting
2019
Energies
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In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series ...
To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/en12061069
fatcat:yqcmprijyfgjvhl4q47d2e5i3a
Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method
2021
Applied Sciences
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However, an efficient and accurate forecasting model is required to study the daily consumption of the consumers from their historical data and forecast the necessary energy demand from the consumer's ...
The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows one to reduce the demand for electricity by studying the daily usage pattern of consumers ...
Hence, for an optimal use of the energy from RESs, it is important to predict the energy demand of customers based on historical measurements. ...
doi:10.3390/app11188612
fatcat:uobwuxyz7zc2regcghqflbnegu
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