Data-driven energy consumption prediction of a university office building using machine learning algorithms

Yesilyurt, Hasan; Dokuz, Yesim; Dokuz, Ahmet Sakir

doi:10.1016/j.energy.2024.133242

Energy

2024

DOI: 10.1016/j.energy.2024.133242

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Data-driven energy consumption prediction of a university office building using machine learning algorithms

Hasan Yesilyurt,

Yesim Dokuz,

Ahmet Sakir Dokuz

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Cited by 4 publications

References 82 publications

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Optimizing energy efficiency and emission reduction: Leveraging the power of machine learning in an integrated compressed air energy storage-solid oxide fuel cell system

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Li,

Bu sinnah

et al. 2024

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An ensemble model for the energy consumption prediction of residential buildings

Mohan,

Pachauri

2025

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Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method

Wang,

Yan,

et al. 2024

Applied Sciences

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In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps has significant potential for energy savings. However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. This study proposes a novel Long Short-Term Memory Attention model for precise prediction of drilling pump energy consumption. By integrating Long Short-Term Memory (LSTM) networks with the Attention mechanism, the model effectively captures complex nonlinear relationships and long-term dependencies in energy consumption data. Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. The proposed model achieved Mean Absolute Error (MAE) values ranging from 5.19 to 10.20 and R2 values close to one (0.95 to 0.98) in four test scenarios, demonstrating excellent predictive performance under complex conditions. The high-precision prediction of drilling pump energy consumption based on this method can support energy optimization and provide guidance for field operations.

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Data-driven energy consumption prediction of a university office building using machine learning algorithms

Cited by 4 publications

References 82 publications

Optimizing energy efficiency and emission reduction: Leveraging the power of machine learning in an integrated compressed air energy storage-solid oxide fuel cell system

Optimizing energy efficiency and emission reduction: Leveraging the power of machine learning in an integrated compressed air energy storage-solid oxide fuel cell system

An ensemble model for the energy consumption prediction of residential buildings

Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method

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