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Research on Natural Gas Short-Term Load Forecasting Based on support Vector Regression

Chinese Journal of Chemical Engineering 2004年第5期12卷 732-736页

作者：刘涵刘丁郑岗梁炎明 School of Automation and Information Engineering Xi'an University of Technology Xi'an 710048 China

Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimum *** learning techniques have been increasingly applied to load forecasting. A novel regression technique based on the statistical learning theory, support vector machines (SVM), is investigated in this paper for natural gas shortterm load forecasting. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional regression techniques. Using a data set with 2 years load values we developed prediction model using SVM to obtain 31 days load predictions. The results on city natural gas short-term load forecasting show that SVM provides better prediction accuracy than neural network. The software package natural gas pipeline networks simulation and load forecasting (NGPNSLF) based on support vector regression prediction has been developed, which has also been applied in practice.

关键词： structure risk minimization support vector machines support vectorregression load forecasting neural network

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A Precise RFID Indoor Localization System with Sensor Network Assistance

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China Communications 2015年第4期12卷 13-22页

作者： ZHANG Dian LU Kezhong MAO Rui Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software EngineeringShenzhen University

Indoor localization is very critical for medical care applications, e.g., the patient localization or tracking inside the building of the hospital. Traditional Radio Frequency Identification(RFID) technologies are very popular in this area since their cost is very low. In such technologies, each tag acts as the transmitter and the Radio Signal Strength Indicator(RSSI) information is measured from the readers. However, RSSI information suffers severely from the multi- path phenomenon. As a result, if in a very large area, the localization accuracy will be affected seriously. In order to solve this problem, we introduce Wireless Sensor Networks(WSNs) with only a few nodes, each of which acts as both transmitter and receiver. In such networks, the change of signal strength(referred as dynamic of RSSI) is leveraged to select a cluster of reference tags as candidates. Then the fi nal target location is estimated by using the RSSI relationships between the target tag and candidate reference tags. Thus, the localization accuracy and scalability are able to be improved. We proposed two algorithms, SA-LANDMARC, and COCKTAIL. Experiments show that the localization accuracy of the two algorithms can reach 0.7m and 0.45 m, respectively. Compared to most traditional Radio Frequency(RF)-based approaches, the localization accuracy is improved at least 50%.

关键词： radio frequency RFID wirelesssensor networks hybrid support vectorregression

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