Industrial Application of Machine Learning Clustering for a Combined Heat and Power Plant: A Pavlodar Case Study

Omirgaliyev, Ruslan; Salkenov, Aldiyar; Bapiyev, Ideyat; Zhakiyev, Nurkhat

doi:10.1109/ukrmico52950.2021.9716605

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The data file (Datasets for Case 1 and Case 2) associated with this article can be found in the online version at [2] . A combination of quantitative and qualitative approaches was used in the data analysis of demand variations and distributions, technical and consumption parameters to provide optimal solutions and increase the efficiency of CHPPs using the proposed optimization model consisting: (i) unit commitment constraints for boilers and turbines; (ii) bounds on the operating levels for boilers and turbines within desired operating regions; (iii) extreme operating regions for turbines; (iv) energy balances for turbines; (v) total electricity and heat balances for electricity and heat network; and (vi) maintenance tasks for units within given time-windows [3] , [4] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Set of the data for modeling large-scale coal-fired combined heat and power plant in Kazakhstan

et al. 2022

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Set of the data for modeling large-scale coal-fired combined heat and power plant in Kazakhstan

et al. 2022

Self Cite

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“…The use of formalized system description for the analysis of energy and environmental policy issues is carried out with the help of modeling of energy systems. In the modeling of electric power systems, it is necessary to understand the importance of complete data collection and analysis for entering into the model [13].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Methods for the Analysis of Heat Energy Consumption by Zones with a Change in Outdoor Temperature: Case Study for Nur-Sultan City

Omirgaliyev¹,

Zhakiyev²,

Aitbayeva³

et al. 2022

IJSDP

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The environmental situation in the capital city is always in the focus of attention of the municipal authorities of the city and is one of the most important factors influencing the decisions. The capital of Kazakhstan, Nur-Sultan, consumes heat energy generated from fossil fuel, and one of the major problems is an extremely cold and long winter. The GHG emissions and particle matters from the coal-based Combined Heat and Power plant have a significant impact on the environment as smog, particularly in the heating season. This work analyses spatial high-resolution Big Data collected from the metering points of 385 houses and 62 heat transmission contours across a city during the heating season. The temporary resolution was 10 minutes i.e., 8754 rows for 5 months (Jan-May). There are shown the findings of the correlation rates analysis between heat energy consumption by zones of Nur-Sultan and ambient temperature, as well as non-efficient zones with substantial losses. In this paper for developing the prediction tools for the Smart City heat consumption there were used mixed modelling methods and machine learning approaches, such as Linear Regression, K-neighbours Regressor, and Random Forest Regressor models. These obtained results could be helpful for predicting optimal pumping pressure for each distribution network using machine learning technologies and finding overheated contours in real time.

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System for Water and Electricity Consumption Prediction in Smart Cities Using ML

Zhakiyev,

Omirgaliyev,

Bapiyev

et al. 2023

2023 IEEE International Conference on Smart Information Systems and Technologies (SIST)

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Industrial Application of Machine Learning Clustering for a Combined Heat and Power Plant: A Pavlodar Case Study

Cited by 3 publications

References 18 publications

Set of the data for modeling large-scale coal-fired combined heat and power plant in Kazakhstan

Set of the data for modeling large-scale coal-fired combined heat and power plant in Kazakhstan

Application of Machine Learning Methods for the Analysis of Heat Energy Consumption by Zones with a Change in Outdoor Temperature: Case Study for Nur-Sultan City

System for Water and Electricity Consumption Prediction in Smart Cities Using ML

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