Load Modeling for Cryptocurrency Mining Devices Using System Identification and Machine Learning

This paper presents a novel flexible load model tailored for cryptocurrency mining applications, specifically designed to dynamically adjust Application Specific Integrated Circuits-based mining operations based on real-time power availability. The model allows miners to use excess power during peak generation and reduce their usage during low power generation, all in support of maintaining a stable grid. The integration of the System Identification technique and some machine learning methods, such as Linear Regression, Support Vector Machine, and Neural Network Models, are used to identify and verify the proposed model against the genuine load data. Values of R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ranging from 0.9898 to 0.9953 point toward a very good agreement between the simulated and actual load profiles. The Neural Network Model presents the lowest RMSE, MSE, MAE, and WIE, which means that the model accurately captures the actual behavior of the load. This approach strengthens the efficiency and accuracy of mining processes and is quite compatible with renewable power generation and, therefore, the effective use of energy. This research forms the reference platform for adaptive load management for such intensive applications to provide a viable approach to renewable energy integration into high-power utilization systems.