Study On Energy Saving And Control Of Gymnasium Illumination

Energy saving is a traditional topic.Recently we talk more about energy savings because of the limited earth resource and its decrease.Now electric energy is mainly converted from natural resources,so that to some extent save electric energy is to save natural resources.Power consumption of illumination accounts for a considerable proportion of the total power consumption.Therefore it has the practical significance to control illuminating system and to save its power consumption.As the 2008 Olympic Games approaches ,the domestic gymnasium are under high-speed construction.It is effective to control the illuminating system of gymnasiums and save power,cause it can not only cut the power consumption and save the cost,but also reduce ullage of lamps and prolong service life of lamps to save the spend.The purpose of the thesis is to devise an intelligent light operator.The device can predict voltage and regulate illumination automatically.The thesis explains from background of subject,plan demonstration,system hardware and software and research on control algorithm respectively.In the part of plan demonstration,first I choose high pressure sodium lamp as the light sources after doing researches on choice of illuminated light sources.Then I enumerate several methods of voltage regulation at home and abroad.By making sufficient comparison,I select an intelligent light operator.In the part of hardware design,the author makes the detailed explanation to several main circuits,especially for the vital part-binary regulator.In the part of software design,I write out the flow chart of program combined with hardwire working and process of algorithm.In the most important part-research on algorithm,I demonstrate the rationality and the feasibility of voltage prediction.Then I introduce the predictive tool-neural network and its algoritm.Afterward,I give a detailed explanation to the prediction model and its simulation on MATLAB.By comparison between the two algorithms,I adopt LM algorithm to train the network.At last I present the analysis of simulation result.The result proves validity and practicability of the prediction.