Study On Classification Of Public Building And Load Prediction Method

As we all know, energy is the bottleneck of the rapid development of society, and energy conservation is the duty of every citizen. In our country, due to the rapid growth of economic, the energy demand is increasing year after year. As the main part of total energy consumption in society, the increasing speed of building energy consumption is so fast that the saving potential is very big in the field of building, which is of significance to further realize the goal of our country's energy strategy and sustainable development. Since air conditioning system is the most important influencing factor for energy consumption, the accurate calculation of building loads especially the cooling load should be started first.At first, this paper analyzes the influencing factors of air conditioning load and the existing methods of load forecasting, which we learn the HVAC designers tend to use the computer simulation and estimation method to calculate the cooling load. However, the input parameters of computer simulation method are too many and the time-consuming is too much so that it is not easy to master for people. And in the existing specifications and design standards, estimating indicators are roughly classified only according to building function, which is easy to calculate the air conditioning load larger and result in the waste of energy. So simplifying the calculation method without reducing the precise and analyzing the main factors of cooling load is the main research content in forecasting cooling load field at present.Through the qualitative and quantitative analysis of influencing factors, factors which are keys to cooling load are selected. Finally, there are six factors which are chose such as geographical location of building, architecturalfeatures, shape coefficient, window to wall ratio, the integrated heat transfer coefficient,indoor capita density for classification indexes to classify the existing public building,and a series of benchmark models are established. In addition, the energy consumption simulation software DesignBuilder is used for annual cooling load characteristics analysis of each kind of model. At last, this paper choose the 51th large cooling load per unit area as the cooling load index of model, and thecooling loadindex database of public buildings is established, which can provide reference and guidance for cooling load prediction of public buildings.Finally, the research of public buildings in Changsha is an example to verify theapplicability of database. Through the analysis of theactual operation load values, selection values from database and design values in design phase by estimating method of the office buildings, commercial buildings, hotels and educational buildings, we know the accuracy using values from database which is built in this paper for cooling load calculation is enough to satisfy the engineering applicability, and the error is in the range of allowable of engineering. And at the same time, as long as some parameters are knew in the urban planning stage, the database can be also applied to calculate the cooling load.In the last, I hope the cooling loadindex database of public buildings can provide some theoretical basis and engineeringapplication value for the cooling load prediction of public buildings.