Notice of Retraction: Well Cumulative Production Time Series Prediction Model

2010 ◽  
Author(s):  
Jiao Yuwei ◽  
Zheng Songqing ◽  
Zhou Xinmao ◽  
Zhang Jing
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Production Time ◽  
Cumulative Production

Tomato Prices Time Series Prediction Model Based on Wavelet Neural Network

2014 ◽  
Vol 644-650 ◽  
pp. 2636-2640 ◽  
Author(s):  
Jian Hua Zhang ◽  
Fan Tao Kong ◽  
Jian Zhai Wu ◽  
Meng Shuai Zhu ◽  
Ke Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Agricultural Production ◽  
Supply And Demand ◽  
Time Series Prediction ◽  
Wavelet Neural Network ◽  
Agricultural Products ◽  
Price Model ◽  
Agricultural Prices

Accurate prediction of agricultural prices is beneficial to correctly guide the circulation of agricultural products and agricultural production and realize the equilibrium of supply and demand of agricultural area. On the basis of wavelet neural network, this paper, choosing tomato prices as study object, tomato retail price data from ten collection sites in Hebei province from January, 1st, 2013 to December, 30th, 2013 as samples, builds the tomato price time series prediction model to test price model. As the results show, model prediction error rate is less than 0.01, and the correlation (R2) of predicted value and actual value is 0.908, showing that the model could accurately predict tomatoes price movements. The establishment of the model will provide technical support for tomato market monitoring and early warning and references for related policies.

Based on Time Series Prediction of Photovoltaic Power Plant Output

2011 ◽  
Vol 383-390 ◽  
pp. 5142-5147
Author(s):  
Wei Guo Li ◽  
Zhi Min Liao ◽  
Xue Lin Sun
Keyword(s):  
Time Series ◽  
Power Systems ◽  
Prediction Model ◽  
Time Series Prediction ◽  
System Capacity ◽  
Stable Operation ◽  
Power Prediction ◽  
Pv System ◽  
Test And Evaluation ◽  
System Output

With the PV power system capacity continues to expand, PV power generation forecasting techniques can reduce the PV system output power of randomness, it has great impact on power systems. This paper presents a method based on ARMA time series power prediction model. With historical electricity data and meteorological factors, the model gets test and evaluation by Eviews software. Results indicated that the prediction model has high accuracy, it can solve the shortcomings of PV randomness and also can improve the ability of the stable operation of the system.

Time series prediction model for sequential learning

2007 ◽  
Vol 90 (12) ◽  
pp. 129-139
Author(s):  
Manabu Gouko ◽  
Yoshihiro Sugaya ◽  
Hirotomo Aso
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Sequential Learning

scholarly journals Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guorong Zhu ◽  
Sha Peng ◽  
Yongchang Lao ◽  
Qichao Su ◽  
Qiujie Sun
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Electricity Consumption ◽  
Operating Efficiency ◽  
Short Term ◽  
Model Based ◽  
Consumption Data ◽  
Consumption Prediction

Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.

scholarly journals Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation

2020 ◽  
Vol 185 ◽  
pp. 01052
Author(s):  
Runjie Shen ◽  
Ruimin Xing ◽  
Yiying Wang ◽  
Danqiong Hua ◽  
Ming Ma
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Random Forest ◽  
Prediction Model ◽  
Power Plants ◽  
Time Series Prediction ◽  
Cloud Amount ◽  
Continuous Model ◽  
Short Term ◽  
Photovoltaic Power

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model.

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