Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network

Abstract

Massive energy consumption data of buildings was generated with the development of information technology, and the real-time energy consumption data was transmitted to energy consumption monitoring system by the distributed wireless sensor network (WSN). Accurately predicting the energy consumption is of importance for energy manager to make advisable decision and achieve the energy conservation. In recent years, considerable attention has been gained on predicting energy use of buildings in China. More and more predictive models appeared in recent years, but it is still a hard work to construct an accurate model to predict the energy consumption due to the complexity of the influencing factors. In this paper, 40 weather factors were considered into the research as input variables, and the electricity of supermarket which was acquired by the energy monitoring system was taken as the target variable. With the aim to seek the optimal subset, three feature selection (FS) algorithms were involved in the study, respectively: stepwise, least angle regression (Lars), and Boruta algorithms. In addition, three machine learning methods that include random forest (RF) regression, gradient boosting regression (GBR), and support vector regression (SVR) algorithms were utilized in this paper and combined with three feature selection (FS) algorithms, totally are nine hybrid models aimed to explore an improved model to get a higher prediction performance. The results indicate that the FS algorithm Boruta has relatively better performance because it could work well both on RF and SVR algorithms, the machine learning method SVR could get higher accuracy on small dataset compared with the RF and GBR algorithms, and the hybrid model called SVR-Boruta was chosen to be the proposed model in this paper. What is more, four evaluate indicators were selected to verify the model performance respectively are the mean absolute error (MAE), the mean squared error(MSE), the root mean squared error (RMSE), and the R-squared (R2), and the experiment results further verified the superiority of the recommended methodology.

Machine learning and deep learning methods for wireless network applications

“…Five papers on wireless sensor networks are listed as follows: (1) "A bi-population QUasi-Affine TRansformation Evolution algorithm for global optimization and its application to dynamic deployment in wireless sensor networks, " by Nengxian Liu, Jeng-Shyang Pan, and Trong-The Nguyen from China and Vietnam [13]; (2) "Simplified clustering and improved intercluster cooperation approach for wireless sensor network energy balanced routing, " by Yanxin Yao, Wei Chen, Jie Guo, Xiaoyu He, and Ruixuan Li from China [14]; (3) "Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network, " by Ni Guo, Weifeng Gui, Wei Chen, Xin Tian, Weiguo Qiu, Zijian Tian, and Xiangyang Zhang from China [15]; (4) "A signal reconstruction method of wireless sensor network based on compressed sensing, " by Shiyu Zhu, Shanxiong Chen, Xihua Peng, Hailing Xiong, and Sheng Wu from China [16]; and (5) "Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification, " by Shu-Chuan Chu, Thi-Kien Dao, Jeng-Shyang Pan, and Trong-The Nguyen from China and Vietnam [17]. Detailed information of each article could be found in [13][14][15][16][17].…”

Machine Learning Methods to Investigate Drug Delivery in Infusion Pump

“…Support Vector Regression Support Vector Machine (SVM) is a typical supervised learning method in machine learning. It is a classifier and performs regression is termed as SVR [31]. SVR is based on the concept of minimization of structural risk hypothesis, which lessens both empirical risk and the confidence interval of the learning machine.…”

“…The prediction of this variable has been analysed for its accuracy using the prediction model considered in this current work. The statistical metrics utilized to identify the performance of LR, PCR, and SVR approach are mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination(R 2 ) [31]. MSE, RMSE, and MAE are used to identify the prediction error of the predictive models.…”

Machine learning approach to predict delay in smart infusion pump

“…Statistical metrics such as prediction accuracy, Mean Absolute Error (MAE), root mean square error (RMSE), and coefficient of determination (R 2 ) were found. (24) The evaluation was carried out for two different Q c for the set of drugs considered. Q c considered in this study are 10 ml/hr and 50 ml/hr.…”