论文标题
使用机器学习和神经网络预测发电厂的燃料消耗
Predicting Fuel Consumption in Power Generation Plants using Machine Learning and Neural Networks
论文作者
论文摘要
来自国家电网发电的不稳定导致行业（例如电信）依靠植物发电机来经营其业务。但是，这些次要发电机会带来其他挑战，例如进出系统的燃油泄漏以及燃油水平计的扰动。因此，电信运营商一直参与燃料提供柴油发电机的不断需求。随着社会经济因素造成的燃油价格上涨，过度的燃料消耗和燃料盗用成为一个问题，这会影响网络公司的平稳运行。在这项工作中，我们比较了四种机器学习算法（即梯度提升，随机森林，神经网络和套索），以预测发电厂消耗的燃料量。在评估了这些模型的预测准确性之后，梯度增强模型的表现超过了其他三个回归变量模型，其NASH效率最高为99.1％。
The instability of power generation from national grids has led industries (e.g., telecommunication) to rely on plant generators to run their businesses. However, these secondary generators create additional challenges such as fuel leakages in and out of the system and perturbations in the fuel level gauges. Consequently, telecommunication operators have been involved in a constant need for fuel to supply diesel generators. With the increase in fuel prices due to socio-economic factors, excessive fuel consumption and fuel pilferage become a problem, and this affects the smooth run of the network companies. In this work, we compared four machine learning algorithms (i.e. Gradient Boosting, Random Forest, Neural Network, and Lasso) to predict the amount of fuel consumed by a power generation plant. After evaluating the predictive accuracy of these models, the Gradient Boosting model out-perform the other three regressor models with the highest Nash efficiency value of 99.1%.
