Power Supply Coal Consumption Prediction Model Based on Long and Short-term Memory Neural Network

Mengjie Liu; Chenggang Zhen

doi:10.54097/fcis.v3i1.6345

Authors

Mengjie Liu
Chenggang Zhen

DOI:

https://doi.org/10.54097/fcis.v3i1.6345

Keywords:

Power supply coal consumption, MRMR, LSTM

Abstract

As a measure of the overall economic performance of thermal power plants and an important component of the variable cost of power generation, the prediction of coal consumption of power supply is of great significance for the bidding decision of power generation companies. In this paper, we propose an improved long and short-term memory (LSTM) model for calculating the coal consumption of thermal power units, using a large amount of data stored in the plant monitoring information system (SIS). Firstly, the data are pre-processed using the threshold determination method to filter out the data of stable operating conditions. Secondly, the maximum correlation minimum redundancy (mRMR) algorithm is used to determine the optimal set of special features. The results show that the prediction effect of the improved LSTM-based power supply coal consumption calculation model proposed in this paper is better than other models, and the calculation accuracy is higher, which is suitable for power supply coal consumption calculation.

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