Predicting Bitcoin Mining Energy Consumption Using Machine Learning: A Case for k-Nearest Neighbors Regression

Nazmiye Eligüzel; Sena Aydoğan

Article Details

Predicting Bitcoin Mining Energy Consumption Using Machine Learning: A Case for k-Nearest Neighbors Regression

Authors:

Nazmiye Eligüzel | Sena Aydoğan

Publisher:

Bitlis Eren University

Main Topics:

Industrial Engineering

Journal:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.561-582

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001343832

pISSN:

2147-3129

Keywords:

Abstract:

 The energy consumption of Bitcoin mining has emerged as a critical topic in cryptocurrency research, influenced by the significant environmental and economic impacts of blockchain activities. This study examines the energy consumption of Bitcoin mining with a dataset that includes essential blockchain variables such as overall hash rate, network difficulty, daily confirmed transactions, mempool size, average block size, and daily Bitcoin output. A new energy consumption indicator is proposed to contribute to the research domain. The proposed indicator better accurately reflects the dynamics of blockchain energy utilization. Various machine learning models, such as Random Forest, Gradient Boosting, Support Vector Regression, and Multi-layer Perceptron, are evaluated, with particular emphasis on k-Nearest Neighbors Regression k-NNR. The k-NNR model surpassed all other models, with a 2 value of 0.80427 and a Mean Squared Error MSE of 0.00441, indicating its high prediction accuracy. Analysis of feature importance indicated that daily Bitcoin production and block size are significant determinants of energy use. The findings underscore the efficacy of k-NNR in energy modeling, offering insights into Bitcoins energy dynamics and establishing a foundation for more energy-efficient blockchain systems.

 The energy consumption of Bitcoin mining has emerged as a critical topic in cryptocurrency research, influenced by the significant environmental and economic impacts of blockchain activities. This study examines the energy consumption of Bitcoin mining with a dataset that includes essential blockchain variables such as overall hash rate, network difficulty, daily confirmed transactions, mempool size, average block size, and daily Bitcoin output. A new energy consumption indicator is proposed to contribute to the research domain. The proposed indicator better accurately reflects the dynamics of blockchain energy utilization. Various machine learning models, such as Random Forest, Gradient Boosting, Support Vector Regression, and Multi-layer Perceptron, are evaluated, with particular emphasis on k-Nearest Neighbors Regression k-NNR. The k-NNR model surpassed all other models, with a 2 value of 0.80427 and a Mean Squared Error MSE of 0.00441, indicating its high prediction accuracy. Analysis of feature importance indicated that daily Bitcoin production and block size are significant determinants of energy use. The findings underscore the efficacy of k-NNR in energy modeling, offering insights into Bitcoins energy dynamics and establishing a foundation for more energy-efficient blockchain systems.

 The energy consumption of Bitcoin mining has emerged as a critical topic in cryptocurrency research, influenced by the significant environmental and economic impacts of blockchain activities. This study examines the energy consumption of Bitcoin mining with a dataset that includes essential blockchain variables such as overall hash rate, network difficulty, daily confirmed transactions, mempool size, average block size, and daily Bitcoin output. A new energy consumption indicator is proposed to contribute to the research domain. The proposed indicator better accurately reflects the dynamics of blockchain energy utilization. Various machine learning models, such as Random Forest, Gradient Boosting, Support Vector Regression, and Multi-layer Perceptron, are evaluated, with particular emphasis on k-Nearest Neighbors Regression k-NNR. The k-NNR model surpassed all other models, with a 2 value of 0.80427 and a Mean Squared Error MSE of 0.00441, indicating its high prediction accuracy. Analysis of feature importance indicated that daily Bitcoin production and block size are significant determinants of energy use. The findings underscore the efficacy of k-NNR in energy modeling, offering insights into Bitcoins energy dynamics and establishing a foundation for more energy-efficient blockchain systems.

 The energy consumption of Bitcoin mining has emerged as a critical topic in cryptocurrency research, influenced by the significant environmental and economic impacts of blockchain activities. This study examines the energy consumption of Bitcoin mining with a dataset that includes essential blockchain variables such as overall hash rate, network difficulty, daily confirmed transactions, mempool size, average block size, and daily Bitcoin output. A new energy consumption indicator is proposed to contribute to the research domain. The proposed indicator better accurately reflects the dynamics of blockchain energy utilization. Various machine learning models, such as Random Forest, Gradient Boosting, Support Vector Regression, and Multi-layer Perceptron, are evaluated, with particular emphasis on k-Nearest Neighbors Regression k-NNR. The k-NNR model surpassed all other models, with a 2 value of 0.80427 and a Mean Squared Error MSE of 0.00441, indicating its high prediction accuracy. Analysis of feature importance indicated that daily Bitcoin production and block size are significant determinants of energy use. The findings underscore the efficacy of k-NNR in energy modeling, offering insights into Bitcoins energy dynamics and establishing a foundation for more energy-efficient blockchain systems.

Article Details

Predicting Bitcoin Mining Energy Consumption Using Machine Learning: A Case for k-Nearest Neighbors Regression

Authors:

Nazmiye Eligüzel | Sena Aydoğan

Publisher:

Bitlis Eren University

Main Topics:

Industrial Engineering

Journal:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.561-582

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001343832

pISSN:

2147-3129

Bitcoin

Energy consumption

Hash rate

k-Nearest Neighbor Regression

Machine learning

Issues

Article Details

Predicting Bitcoin Mining Energy Consumption Using Machine Learning: A Case for k-Nearest Neighbors Regression

Authors: Nazmiye Eligüzel | Sena Aydoğan

Publisher: Bitlis Eren University

Main Topics: Industrial Engineering

Journal: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.561-582

Document Type: Article

Publication Date: 3/2025

Document Language: English

Document ID (Org No.): 2001343832

pISSN: 2147-3129

Bitcoin

Energy consumption

Hash rate

k-Nearest Neighbor Regression

Machine learning

Issues

Authors:

Nazmiye Eligüzel | Sena Aydoğan

Publisher:

Bitlis Eren University

Main Topics:

Industrial Engineering

Journal:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 1, pp.561-582

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001343832

pISSN:

2147-3129