PLUG LOAD IDENTIFICATION IN EDUCATIONAL BUILDINGS USING MACHINE LEARNING ALGORITHMS
Plug loads accounts for 20% to 30% of building energy consumption and has an increasing trend. Automatic plug load identification is one of the technique for effectively managing the plug load consumption. There are several studies on Non Intrusive load monitoring (NILM) but limited studies on Intrusive load monitoring (ILM). ILM is a technique that uses a low end power meter on every plug load to monitor it’s power consumption. In this paper, machine-learning techniques are applied on low-frequency ILM data for identification of plug load and it’s state (ON/Sleep). The results show the identification accuracies are close to 98%.
In the past 19 years,while China's urbanization was fast-developing,a series of problems related to urbanization were there upon production.In this paper,based on spectrum characteristic of knowledge method,the thematic information of three periods of LUCC were extracted from Landsat TM,ETM + and CBERS-2.Combing with the GIS technology,the land-use transfer matrix from 1987 to 2006 were obtained.Besides,the LUCC models were applied to analyze the characteristic of land use change from 1987 to 2006.Based on the land-use change map and land-use transfer matrix,spatial dynamic characteristic and land-use conversion of the study area were analyzed.The research could provide suggestions for land management and making policy for local government.