daily coal consumption of power plantsARIMA modelshort-term prediction煤炭日耗数据是反映工业活动的重要指标,具有较强的跟踪意义,研究短期内沿海电厂煤炭日耗变化有助于预判煤炭市场走势及需求变化情况.运用时间序列分析法对沿海电厂煤炭日耗数据进行建模,得到适用的ARIMA模型后,对后续日耗数据进行短期预测,取得了较好...
As an important link in the complex system engineering project of open pit mining, the quality of the boundary determines the performance of the project to a large extent. However, changes in economic indicators may raise doubts about the optimality of m
1)ARIMA model predictionARIMA模型预测 英文短句/例句 1.Oil Price Forecast In 2007 Based on ARIMA Model;利用ARIMA模型预测2007年油价走势 2.Apply ARIMA model to forecast China s coal production;应用ARIMA模型预测中国煤炭生产 3.Analysis of Time-series Forecast for Blend Price of Steel Based on ARIMA...
As the main objective of this research is to develop an energy consumption predictive model for smart commercial building by using several machine learning methods in a cloud-based machine learning platform, this research focuses more on the accuracy of the methodology applied in predicting energy con...
However, the selection of wavelet bases in the WT method is difficult, and decomposition results based on different wavelet bases are different. Liu et al. [26] pointed out that it is difficult to achieve high volatility and autocorrelation wind speed prediction by using a single model, and ...
Slope instabilities in open-pit mines pose a safety risk to workers and a financial burden on production. The direct impact of slope stability on safety an
4. CO2 Emission-Concentration Prediction with Spatiotemporal Coupled Properties Based on ARIMA-BPNN 4.1. Construction of the ARIMA-BPNN Hybrid Model Linear regression can be considered if the periodic characteristics of a time series are stable over time and there is a correlation between different se...
The model consists of a deep learning network model based on a residual neural network (ResNet), a spatial–temporal attention mechanism, and a convolutional long short-term memory neural network (ConvLSTM). The spatial–temporal attention mechanism is embedded in each residual unit of the ResNet...
It has been found that time series model-based techniques play a pivotal role in monitoring the condition of machinery based on vibration signals [23]. Therefore, an effective time series forecasting model is the key to monitoring the condition of industrial equipment, which can uncover potential ...
By using univariate wind speed time series, this paper proposes a hybrid wind speed prediction model based on Autoregressive Moving Average-Support Vector Regression (ARMA-SVR) and error compensation. First, to explore the balance between the computation cost and the sufficiency of the input features...