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In the integrated coal supply chain, inventory management at downstream power plants is of significant importance. Precise inventory management in power plants ensures their normal operation and provides sufficient capacity to respond to emergency demands, simultaneously, it ensures the coal production capacity and transportation capabilities upstream are not wasted, maintaining a balance between supply and demand, optimizing the supply chain processes, and saving costs. Coal consumption forecasting is a crucial prerequisite for effective coal inventory management in power plants. In recent years, machine learning algorithms have demonstrated strong capabilities in forecasting. However, for some small and medium-sized power plants that are not as fully developed in terms of information technology, they lack a high level of data accumulation, using time series methods for forecasting is a more appropriate choice for such power plants. This paper investigates the prediction of coal consumption in power plants based on Holt-Winters, LSTM, and the ensemble time series algorithm FBProphet. The experimental analysis is carried using historical data of a thermal power plant and the result shows that the FBProphet model has higher accuracy for the prediction of coal consumption. Subsequently, based on the prediction model, this paper also proposes an easily implementable inventory control method, especially suitable for small and medium-sized power plants within the integrated coal supply chain, which provides more guidance for the inventory control methods of power plants.
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