Coal Requirement Prediction Using BP Neural Network

Xu Xin, Xuli Hong
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Abstract

Coal is one of the most important main energy-consuming resources in our society. It is important to forecast the coal requirement with high accuracy. BP neural network forecasting model has the typical of self-learning and self-adaptation. It is often used in these systems that are difficult to create accurate mathematical model. The factors such as the trend of the industrial coal, the rate of increased GDP, rice index and the proportion in the energy-consuming of coal are considered in this paper. We use improved BP model to predict and simulate in MATLAB. It proves that this prediction has better application.
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基于BP神经网络的煤炭需求预测
煤炭是我国社会最重要的主要能源消耗资源之一。准确预测煤炭需求具有重要意义。BP神经网络预测模型具有自学习、自适应的特点。它常用于难以建立精确数学模型的系统中。本文考虑了工业用煤的变化趋势、GDP增长率、稻米指数和煤炭在能源消费中的比重等因素。采用改进的BP模型在MATLAB中进行预测和仿真。结果表明,该预测具有较好的适用性。
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