Research On New Energy Saving Technology Of Architectural Lighting System

With the acceleration of urbanization,the number of buildings has increased rapidly,and the energy consumption of the construction industry has increased year by year.With reference to the development experience of developed countries,China's building energy consumption will continue to grow for a long period of time.If it is not controlled,the building energy consumption will reach an astonishing level in the future.It is very urgent for building energy-saving design and energy consumption research.In building energy consumption,lighting energy consumption is about 30% of total energy consumption,which has huge energy saving potential.Therefore,energy-saving research on lighting systems has become a research hotspot in the field of building energy conservation.Among all types of buildings,the typicality of public buildings is regarded as the key research object of building energy conservation.Analysis of the public building lighting system,the system's high energy consumption problems mainly lies in the defects of control technology and equipment maintenance is not in place.In the actual situation,the architectural lighting system continues to operate when there is no space,resulting in wasted energy.Moreover,after the lighting system is put into use,the long-term operation of the lamp is often caused by the lack of equipment maintenance,resulting in an increase in system energy consumption.In view of these two situations,this paper conducts energy-saving research on the operation and maintenance phases of the lighting system.The research content can be summarized into the following three aspects:(1)Taking public buildings as the research object,analyzing the characteristics of the lighting system,the factors affecting the energy consumption of the lighting system are divided into two major categories: secondary factors and secondary factors.Based on the influence of main factors on the energy consumption of each stage of the lighting system,this paper proposes two energy-saving schemes: optimizing lighting control technology on one hand to reduce system power consumption and achieving direct energy saving of the lighting system;on the other hand,using accurate prediction models,The future energy consumption of the system is predicted,which provides a basis for maintenance personnel to judge the system status and maintenance,and achieve overall system optimization to achieve indirect energy saving.(2)Based on the system control model,this paper studies the relationship between intelligent lighting system and personnel behavior,and establishes an intelligent lighting control model based on personnel displacement.This paper analyzes the current situation of human behavior model,compares the advantages and disadvantages of several human behavior models,and selects the position information obtained by indoor positioning as the displacement information of this paper,which is used as the premise of lamp adjustment.For the characteristics of the model built in this paper,the use of traditional particle swarm optimization algorithm is limited,and the particle swarm optimization algorithm based on penalty function can adapt to this feature.The experimental results show that the model established in this paper can dynamically adjust the lighting equipment in the space according to the changes of external factors,greatly reduce the lighting energy consumption,and realize the optimization of lighting control technology.(3)Based on the overall optimization of the system,this paper studies the energy consumption prediction model of the lighting system for the energy waste caused by the system maintenance.On the basis of analyzing the factors affecting lighting energy consumption,historical energy consumption data,weather conditions,outdoor sunshine duration,and holidays are used to predict the energy consumption of the system.Support vector machine is an ideal method for nonlinear modeling for energy consumption characteristics and small sample size of energy consumption data.The selection of kernel parameters and penalty factors has a key impact on prediction accuracy,so particle swarm optimization with global optimization features is used.Optimize parameter selection.The experimental results show that the improved model has higher prediction accuracy and smaller error than the original support vector machine,which can better perform the energy prediction task in the architectural lighting system.