A Hybrid PSO-BPNN Model Approach for Crude Oil Pipeline Electrical Energy Consumption Forecasting

2020 ◽  
Author(s):  
Lei Xu ◽  
Lei Hou ◽  
Yu Li ◽  
Zhenyu Zhu ◽  
Jiaquan Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Crude Oil ◽  
Back Propagation ◽  
Electrical Energy ◽  
Back Propagation Neural Network ◽  
Electrical Energy Consumption ◽  
Oil Pipeline ◽  
Forecasting Methods ◽  
Bpnn Model

Abstract The electrical energy consumption forecasting for crude oil pipeline is critical in many aspects, such as energy consumption target setting, batch scheduling, unit commitment, etc. For actual crude oil pipelines, the nonlinearity of the sample is strong. The electrical energy consumption of crude pipeline is affected by many parameters, including oil physical property parameter, pipe parameter, station parameter, environmental parameter and operating parameter. At the same time, the whole process has the characteristics of intermittency and complex fluctuations. The above three main reasons make the energy consumption forecasting of crude oil pipeline complicated. In the past few years, several intelligence-based models have been introduced to accurately forecast energy consumption. Among them, back-propagation neural network (BPNN) seems to be more effective and can handle the nonlinear energy behavior and achieve accurate forecast results. However, due to its over-fitting problem, the accuracy of energy consumption forecasting will be reduced. To overcome this problem, the paper proposes a hybrid method for short-term energy consumption forecasting, namely PSO-BPNN. Back propagation neural network is integrated with particle swarm optimization to find optimal network weight. In this research, an effective technique called principal component analysis is applied to eliminate redundant noise and extract the primary characteristics of transportation data. The stratified sampling method is used to divide the training set and the test set to avoid large deviations caused by the randomness of sampling. Taking a crude oil pipeline in northeast china as a case study, SCADA system data are collected daily from December 31, 2016 to June 18, 2019. Comparing the evaluation indicators of PSO-BPNN with that of five state-of-the-art forecasting methods of GA-BPNN, SA-BPNN, DE-BPNN, FOA-BPNN, BPNN, the effectiveness of PSO-BPNN algorithm is evaluated. Compared with other five forecasting methods, the forecast results of PSO-BPNN are in best agreement with the actual data. The results indicate that the proposed PSO-BPNN model outperforms all five models used for comparison, which demonstrates its superior ability to generate forecasts in terms of forecasting accuracy.

Mid-Term Energy Consumption Prediction of Crude Oil Pipeline Pump Unit Based on GSCV-SVM

2020 ◽  
Author(s):  
Lei Xu ◽  
Lei Hou ◽  
Yu Li ◽  
Zhenyu Zhu ◽  
Jiaquan Liu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Crude Oil ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Pump Unit ◽  
Test Set ◽  
Oil Pipeline ◽  
Oil Pipelines ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Abstract Energy consumption prediction plays an important role in pipeline operation regulation and energy management. Accurate energy consumption prediction is helpful to make important decisions, including unit commitment, batch scheduling, load dispatching, energy consumption target setting, etc. The energy consumption of crude oil pipeline is mainly the electrical energy of pump unit. The average annual electrical energy consumption of China’s crude oil pipelines accounts for more than half of the annual operating cost of pipelines. Therefore, the prediction of electrical energy consumption of crude oil pipelines is critical. The energy consumption prediction of crude oil pipelines is very complicated. Firstly, it depends on the variables related to operation parameter, crude oil physical property parameter, environmental parameter and equipment parameter. Secondly, its nonlinearity is strong. Thirdly, the available samples are too little. Through the study on the monthly operation data collected by the Supervisory Control And Data Acquisition (SCADA) system and energy consumption analysis, the turnover and the electrical energy consumption is selected as input variable and output variable, respectively. The support vector machines (SVM) is introduced to predict the monthly electric energy consumption of crude oil pipelines driving oil pumps. However, the generalization capability of SVM is highly dependent on appropriate parameter setting, such as penalty coefficient and kernel parameter. The selection of the optimal parameters is critical to achieving good performance in the learning process. Therefore, in order to improve the generalization ability, GridSearchCV was adopted to optimize the hyperparameters of SVM. Taking a crude oil pipeline from Qinhuangdao City, Hebei Province to Fangshan District, Beijing as an example, the actual operation data for four consecutive years (48 months) are used for this study. The data are divided into training set and test set by stratified sampling method, which consist of 28 samples and 20 samples respectively. The mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) on the test set are 3.42, 21.64, 14.31 and 0.94 respectively. Compared with other five state-of-the-art prediction methods in predictive accuracy, the result shows that GSCV-SVM has the best performance in the case of small samples, and the prediction results are in good agreement with the actual data.

A comparative study of different prediction models in predicting human resource demands

2021 ◽  
pp. 1-11
Author(s):  
Bo Huang
Keyword(s):  
Neural Network ◽  
Human Resources ◽  
Comparative Study ◽  
Human Resource ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Prediction Methods ◽  
Bpnn Model ◽  
Good Agreement

This study analyzed three prediction models: ID model, GM (1,1) model and back-propagation neural network (BPNN) model. Firstly, the principles of the three models were introduced, and the prediction methods of the three models were analyzed. Then, taking enterprise A as an example, the demand for human resources was predicted, and the prediction results of the three models were compared. The results showed that the maximum and minimum errors were 240 people and 12 people respectively in the prediction results of the ID3 model and 64 people and 37 people respectively in the prediction results of the GM (1, 1) model; the errors of the BPNN model were smaller than ten people, and the minimum value of the BPNN model was three people, which was in good agreement with the actual value. The prediction of the human resource demand of enterprise A in the future five years with the BPNN model suggested that the demand for employees would growing rapidly. The results show that the BPNN model has better reliability and can be popularized and applied in practice.

scholarly journals A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network

Technologies ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 30 ◽  
Author(s):  
Muhammad Fayaz ◽  
Habib Shah ◽  
Ali Aseere ◽  
Wali Mashwani ◽  
Abdul Shah
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Energy Consumption ◽  
Data Acquisition ◽  
Residential Buildings ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Statistical Moments ◽  
Feed Forward ◽  
Feed Forward Back Propagation

Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data.

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