A Hybrid Method Based on Svm Integrated Improved Pso Algorithm for Electrical Energy Consumption Forecasting of Crude Oil Pipeline

2021 ◽  
Author(s):  
Lei Xu ◽  
Lei Hou ◽  
Zhenyu Zhu ◽  
Yu Li ◽  
Ting Lei
Keyword(s):  
Energy Consumption ◽  
Crude Oil ◽  
Hybrid Method ◽  
Electrical Energy ◽  
Pso Algorithm ◽  
Electrical Energy Consumption ◽  
Oil Pipeline ◽  
Improved Pso

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 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.

A Hybrid Method Based on SVM Integrated Improved PSO Algorithm for Electrical Energy Consumption Forecasting of Crude Oil Pipeline

2020 ◽  
Author(s):  
Lei Xu ◽  
Lei Hou ◽  
Zhenyu Zhu ◽  
Yu Li ◽  
Ting Lei
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Crude Oil ◽  
Electrical Energy ◽  
Support Vector ◽  
Electrical Energy Consumption ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Oil Pipelines

Abstract Electrical energy consumption forecasting of crude oil pipelines plays a critical role in energy consumption target setting, batch scheduling, and unit commitment. For actual crude oil pipelines, because of its uncertainty, nonlinearity, intermittency, fluctuations and complexity, it is challenging to establish the electrical energy consumption forecasting model. And it is difficult to describe the non-linear characteristics of electrical energy consumption forecasting by traditional methods. Therefore, a novel hybrid electrical energy consumption forecasting system based on the combination of support vector machine (SVM) and improved particle swarm optimization (IPSO) is proposed, which includes four parts: data pre-processing part, optimization part, forecasting part, and evaluation part. In the pre-processing stage, in order to avoid large deviation caused by sampling stochasticity of small samples, the training set and the test set are divided by stratified sampling method. During the modeling process, the non-linear relationship in electrical energy consumption forecasting is efficiently represented by support vector machine, and the parameters of support vector machine regression are optimized by the improved particle swarm optimization algorithm. According to the established IPSO-SVM model, evaluation part is conducted to make a comprehensive evaluation for this framework. By comparing the evaluation indicators of IPSO-SVM with that of eight state-of-the-art forecasting methods, the effectiveness of IPSO-SVM method is evaluated. Based on the operation data of four crude oil pipelines in China, the results show that the proposed IPSO-SVM hybrid model has the best forecasting performance than other benchmark models, and its forecasting results are the closest to the actual data. It is concluded that the proposed approach can be an efficient technique for electrical energy consumption forecasting of crude oil pipelines.

scholarly journals Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xunlin Jiang ◽  
Haifeng Ling ◽  
Jun Yan ◽  
Bo Li ◽  
Zhao Li
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Energy Use ◽  
Particle Swarm ◽  
Electrical Energy ◽  
Pso Algorithm ◽  
Equipment Maintenance ◽  
Electrical Energy Consumption ◽  
Swarm Optimization

Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN) and particle swarm optimization (PSO). A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, and a new adaptive PSO algorithm is proposed for optimizing the parameters of the ANN. Experimental results demonstrate that our approach provides much better accuracies than some other competitive methods on the test data.

A persuasive multi-agent simulator to improve electrical energy consumption

2021 ◽  
pp. 1-15
Author(s):  
Fernanda P. Mota ◽  
Cristiano R. Steffens ◽  
Diana F. Adamatti ◽  
Silvia S. Da C Botelho ◽  
Vagner Rosa
Keyword(s):  
Energy Consumption ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Multi Agent

scholarly journals AUDIT ENERGI PADA SEBUAH HOTEL

2012 ◽  
Vol 16 (3) ◽  
pp. 131
Author(s):  
Didik Ariwibowo
Keyword(s):  
Energy Consumption ◽  
Air Conditioning ◽  
Energy Savings ◽  
Electric Energy ◽  
Electrical Energy ◽  
Hot Water ◽  
Electrical Energy Consumption ◽  
Energy Audit ◽  
Electric Energy Consumption ◽  
Pumping System

Didik Ariwibowo, in this paper explain that energy audit activities conducted through several phases, namely: the initial audit, detailed audit, analysis of energy savings opportunities, and the proposed energy savings. Total energy consumed consists of electrical energy, fuel, and materials in this case is water. Electrical energy consumption data obtained from payment of electricity accounts for a year while consumption of fuel and water obtained from the payment of material procurement. From the calculation data, IKE hotels accounted for 420.867 kWh/m2.tahun, while the IKE standards for the hotel is 300 kWh/m2.tahun. Thus, IKE hotel included categorized wasteful in energy usage. The largest energy consumption on electric energy consumption. Largest electric energy consumption is on the air conditioning (AC-air conditioning) that is equal to 71.3%, and lighting and electrical equipment at 27.28%, and hot water supply system by 4.44%. Electrical energy consumption in AC looks very big. Ministry of Energy and Mineral Resources of the statutes, the profile of energy use by air conditioning at the hotel by 48.5%. With these considerations in the AC target for audit detail as the next phase of activity. The results of a detailed audit analysis to find an air conditioning system energy savings opportunities in pumping systems. Recommendations on these savings is the integration of automation on the pumping system and fan coil units (FCU). The principle of energy conservation in the pumping system is by installing variable speed drives (VSD) pump drive motor to adjust speed according to load on the FCU. Load variations FCU provide input on the VSD pumps to match. Adaptation is predicted pump can save electricity consumption up to 65.7%. Keywords: energy audit, IKE, AC

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