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Residential Electricity Classification Method Based On Cloud Computing Platform and Random Forest

Ming Li, Zhong Fang, Wanwan Cao, Yong Ma, Shang Wu, Yang Guo, Yu Xue, Romany F. Mansour
2021 Computer systems science and engineering  
Based on the distributed architecture of cloud computing, this paper designs an improved random forest residential electricity classification method.  ...  This method uses MapReduce to train an improved random forest model on the cloud computing platform, and then uses the trained model to analyze the residential electricity consumption data set, divides  ...  Acknowledgement: This work was supported by the I6000 migration to the cloud micro-application pilot construction project of the Information and Communication Branch of State Grid Anhui Electric Power  ... 
doi:10.32604/csse.2021.016189 fatcat:wnaga7goxnb37dpnltloqzxohe


M. S. A. Mohd Rapheal, A. Farhana, M. R. Mohd Salleh, M. Z. Abd Rahman, Z. Majid, I. A. Musliman, A. F. Abdullah, Z. Abd Latif
2022 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The points classified as other classes were used for overhead powerline and electricity poles classification using random forest-based Machine Learning (ML) approach in LiDAR 360 software.  ...  Based on the classified point clouds, detailed characteristics of electricity poles (i.e. number of poles, height, diameter and inclination from ground) and overhead powerlines (number of cable segments  ...  A big appreciation to Mohd Radhie Mohd Salleh, as being a part of my thesis journey that was supervised and guided on how to do data pre-processing.  ... 
doi:10.5194/isprs-archives-xlvi-4-w3-2021-239-2022 fatcat:amm2qlyb3rhfbom7kbjdujbtym

Spatio-Temporal Land-Use/Land-Cover Change Dynamics in Coastal Plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine

Yinghui Zhao, Ru An, Naixue Xiong, Dongyang Ou, Congfeng Jiang
2021 Land  
The proposed model uses a random forest algorithm to assist the land-use classification.  ...  However, comprehensive remote sensing image-based land-use analysis is challenged by the lack of massive remote sensing images and the massive computing power of large-scale server systems.  ...  Classification Based on Random Forests Algorithm The main parameters of a random forest classifier based on GEE include the number of classification trees, the number of variables of each classification  ... 
doi:10.3390/land10111149 fatcat:jadvihrjgvbj7nbu5i7as3cxzm

HSIC Bottleneck based Distributed Deep Learning Model for Load Forecasting in Smart Grid with A Comprehensive Survey

Md. Akhtaruzzaman, Mohammad Kamrul Hasan, S. Rayhan Kabir, Siti Norul Huda Sheikh Abdullah, Muhammad Jafar Sadeq, Eklas Hossain
2020 IEEE Access  
[142] 2015 BPNN, Soft computing, Fuzzy logic, Fuzzy Inductive Reasoning, Random Forest Hybrid machine learning methodologies based STLF process STLF 1) Learning error 2) Training and computational  ...  FIGURE 4 . 4 Classification of load forecasting. 1 ) 1 Load demand of different hours and different days 2) Load demand of residential buildings 3) Individual residential electric load 4) Peak load 5  ... 
doi:10.1109/access.2020.3040083 fatcat:tsqokovkm5gpfdsnm7bph73piu

IoT based Machine Learning Automation Algorithm for Controlling the Industrial Loads

Bhagirathi Rao, Acharya Institute of Technology, Hanume Chandrakanth, Sambhram Institute of Technology
2021 International Journal of Intelligent Engineering and Systems  
Additionally, the prediction and error detection utilized in the machine learning process for this Improved Random Forest (IRF) method.  ...  The IRF method uses only eight devices to control and monitor more than thirty devices and this entire process is represented as IoT based Machine Learning (ML) Automation Algorithm.  ...  Random forest, SVM and neural network have been achieved 87.48 %, 85.83 % and 71.66 % of prediction accuracy on 108 datasets.  ... 
doi:10.22266/ijies2021.0831.14 fatcat:lc4op4tlznabhjjrdkl64urx5m

Data science for building energy management: A review

Miguel Molina-Solana, María Ros, M. Dolores Ruiz, Juan Gómez-Romero, M.J. Martin-Bautista
2017 Renewable & Sustainable Energy Reviews  
The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems.  ...  The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies. * Corresponding author Email addresses: miguelmolina  ...  Acknowledgements This research was partially funded by the Spanish Government (TIN2012-30939 and TIN2015-64776-C3-1-R projects), the Andalusian Regional Government (P11-TIC7460 project), the European Commission  ... 
doi:10.1016/j.rser.2016.11.132 fatcat:kc2qhyvbxjcdzczfx5upxmtaxi

Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings

Tania Cerquitelli, Giovanni Malnati, Daniele Apiletti
2019 Energies  
a classification model, the random forest classifier, to forecast a coarse consumption level.  ...  data-processing platform, and can natively scale to huge datasets.  ...  Funding: This work has been partially funded by the EU under the H2020 EnABLES project, Grant Agreement n. 730957, and the SmartData@Polito center for Data Science and Big Data technologies, Politecnico  ... 
doi:10.3390/en12152933 fatcat:ysaloj2cdfapzbgd7pmq5qocve

Land use and land cover changes along the China-Myanmar Oil and Gas pipelines – Monitoring infrastructure development in remote conflict-prone regions

Thiri Shwesin Aung, Thomas B. Fischer, John Buchanan, Stephen P. Aldrich
2020 PLoS ONE  
Drawing from very-high-resolution-multi-temporal-satellite-imagery, this paper reports on a study which employed the Random Forest Classifier and Land Change Modeler to derive detailed information of the  ...  However, afforestation areas can be of a lower value, and in order to be able to take quality of forests into account, it is of crucial importance to accompany satellite-imagery based techniques with field  ...  Based on the results from stratified random sampling method, the overall accuracy of the classification was 62.16% for 2010 and 61.86% for 2012.  ... 
doi:10.1371/journal.pone.0237806 pmid:32813694 fatcat:zhuhf2hzzfha3f6dvqf56jcj6a

Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China

Quanlong Feng, Jiantao Liu, Jianhua Gong
2015 Water  
improved classification accuracy significantly; (3) Random Forest outperformed maximum likelihood and artificial neural network, and showed a similar performance to support vector machine.  ...  The results demonstrate that UAV can provide an ideal platform for urban flood monitoring and the proposed method shows great capability for the accurate extraction of inundated areas.  ...  Particular thanks to the anonymous referees, Academic Editor, and Assistant Editor for their very useful suggestions and comments of our paper.  ... 
doi:10.3390/w7041437 fatcat:4o37btenlbdohgkiizxbxxkx3a

Attendee List

2020 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)  
price-based demand response program in smart grid Pakistan Taimoor Ahmad 203 Predictive Analytics Model Based on Multiclass Classification for Asthma Severity by Using Random Forest Algorithm  ...  occupants' activities in residential households South Africa Agnes Ramokone 102 Platform-independent Infotainment and Digital Cluster Development using Yocto Project Turkey Mustafa Ozcelikors  ... 
doi:10.1109/icecce49384.2020.9179198 fatcat:ryry4suqzrfh3ch2v3veh4z6ei

Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis

Usman Ali, Mohammad Haris Shamsi, Cathal Hoare, Eleni Mangina, James O'Donnell
2021 Energy and Buildings  
based on limited available resources.  ...  Furthermore, this paper proposes a generalized framework based on existing literature for different urban energy modeling methods.  ...  The opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the SFI.  ... 
doi:10.1016/j.enbuild.2021.111073 fatcat:rsr7gemb3jhozaa3l7wktrqk3q

Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency

Hooman Farzaneh, Ladan Malehmirchegini, Adrian Bejan, Taofeek Afolabi, Alphonce Mulumba, Precious P. Daka
2021 Applied Sciences  
In addition to elaborating on the principles and applications of the AI-based modeling approaches widely used in building energy use prediction, an evaluation framework is introduced and used for assessing  ...  Finally, the paper includes a discussion on the open challenges and future directions of research on the application of AI in smart buildings.  ...  Acknowledgments: The authors wish to thank the editor and the reviewers for their contributions on the paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11020763 fatcat:3ipak4rmyba67jdrds6fpyuple

A Smart Autonomous Time- and Frequency-Domain Analysis Current Sensor-Based Power Meter Prototype Developed over Fog-Cloud Analytics for Demand-Side Management

Yung-Yao Chen, Yu-Hsiu Lin
2019 Sensors  
Advanced AI well trained offline in cloud analytics is autonomously and automatically deployed onsite on the prototype as edge analytics at the edge of the Internet for online load identification in DSM  ...  In this study, a smart autonomous time and frequency analysis current sensor-based power meter prototype, a novel IoT end device, in an edge analytics-based artificial intelligence (AI) across IoT (AIoT  ...  Acknowledgments: The authors would like to sincerely thank the reviewers for their valuable comments and suggestions on this study.  ... 
doi:10.3390/s19204443 fatcat:my2fms77lbe7hi3hxk4cxaxfyi

A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches

Sunil Kumar Mohapatra, Sushruta Mishra, Hrudaya Kumar Tripathy, Akash Kumar Bhoi, Paolo Barsocchi
2021 Energies  
Machine learning and deep learning-based models enhance the precision and robustness compared to traditional approaches, making it more reliable.  ...  With technological advancements, computational intelligence models have been successfully contributing to the prediction of the consumption of energy.  ...  and genetic algorithm-based methods.  ... 
doi:10.3390/en14133900 fatcat:czgnt2tfp5av7ep4hluejzgtyy

High Performance Energy Prediction using Hadoop with Spark

Hung Duong-Ngoc, Hoan Nguyen-Thanh, Tam Nguyen-Minh, R. Briš, Gyu Whan Chang, Chu Duc Khanh, M. Razzaghi, K. Stempak, Thanh Toan Phan
2018 ITM Web of Conferences  
Then, the data was analyzed using the scalable machine learning algorithms -MLib was supported and developed on the Spark/SPARKNET platform.  ...  Thereby predict future energy trends and build bases for the system model.  ...  Acknowledgement This study is conducted under the "Big Data Technologies and Applications Project (3/4)" of the Institute for Information Industry which is subsidized by the Ministry of Economic Affairs  ... 
doi:10.1051/itmconf/20182002012 fatcat:qiye2vthsfhq3lrjwkiiqghilm
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