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Andrychowicz, M., et al. (2016) Learning to Learn by Gradient Descent by Gradient Descent. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, 5-10 December 2016, 3988-3996.

has been cited by the following article:

• TITLE: Linear Regression and Gradient Descent Method for Electricity Output Power Prediction

  AUTHORS: Yuanliang Liao

  KEYWORDS: Machine Learning, Linear Algebra, Linear Regression, Gradient Descent, Error Analysis

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.12, December 16, 2019

  ABSTRACT: Regulating the power output for a power plant as demand for electricity fluctuates throughout the day is important for both economic purpose and the safety of the generator. In this work, gradient descent method together with regularization is investigated to study the electricity output related to vacuum level and temperature in the turbine. Ninety percent of the data was used to train the regression parameters while the remaining ten percent was used for validation. Final results showed that 99% accuracy could be obtained with this method. This opens a new window for electricity output prediction for power plants.