Energy-saving Potential Analysis Of One University In Tianjin And The Prediction Of Energy Consumption Based On The Energy Consumption Investigating

To construct economical campus meets requirements of sustainable development of college. The subject on how to construct economical campus was studied home and abroad, there being a big limitation. Taking TianJin University for example, this paper proposes systematically how to launch subjects of investigating and prediction of energy consumption and the analysis of energy-saving against the energy consumption of campus.The investigating of energy consumption includes researches of energy bills and important energy-using systems. We can obtain the status of campus energy consumption and define important energy-using buildings and systems by the research of energy bills. Then, launching the detailed research against important energy-using systems is to find out problems of lighting system, heating system and hot water system by the research of equipment efficiency, system efficiency and management.Energy saving innovation is that proposing reasonable energy-saving measures against the unreasonable situation for energy-using. This paper use methods of estimation and simulation to analyze the potential of energy-saving from the view of technology, management and behavior. The total potential of campus energy-saving is 14.9%. Taking energy consumption of 2007, it can save 3820t Standard coal. There into, the energy-using system which make the great energy-saving contribution is heating system, whose contribution rate is 9.33%; the energy-using buildings which make the great energy-saving contribution is dormitory, whose contribution rate is 6.49%.Energy consumption prediction provides the fundamental basis for college making long-term energy planning and development strategy, and the prediction also help to make a scientific evaluation to the campus energy consumption. This paper defines 5 factors for the energy consumption, GDP, disposable income of urban residents, number of students, construction area and research funding. Then, based on matlab neural network toolbox, a BP neural prediction model was established. The model completes the prediction through training and simulation. The result of prediction show that artificial neural network can fit any nonlinear function, compared with traditional methods, and it is accurate, simple and so on.