Electrical Energy Consumption Prediction Using Machine Learning

2019 ◽  
pp. 72-82
Author(s):  
Simon Stankoski ◽  
Ivana Kiprijanovska ◽  
Igor Ilievski ◽  
Jovanovski Slobodan ◽  
Hristijan Gjoreski
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

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.

scholarly journals Research on Energy Consumption Prediction Based on Machine Learning

2021 ◽  
Vol 791 (1) ◽  
pp. 012100
Author(s):  
Ren Liu ◽  
Zhonghang Wang ◽  
Haihong Chen ◽  
Jie Yang
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

scholarly journals Machine learning for energy consumption prediction and scheduling in smart buildings

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Safae Bourhnane ◽  
Mohamed Riduan Abid ◽  
Rachid Lghoul ◽  
Khalid Zine-Dine ◽  
Najib Elkamoun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Smart Buildings ◽  
Energy Consumption Prediction ◽  
Consumption Prediction
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