Indoor illumination estimation method based on BP neural network algorithm

A BP neural network and algorithm technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as unbalance, and achieve the effect of satisfying energy saving and improving effective utilization.
CN110334387AActive Publication Date: 2019-10-15CHONGQING UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
CHONGQING UNIV
Publication Date
2019-10-15

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Abstract

The invention relates to an indoor illumination estimation method based on a BP neural network algorithm, and belongs to the technical field of intelligent algorithms. The method comprises the following steps: S1, analyzing parameters obtained through traditional formula calculation and a sensor to obtain BP neural network input parameters and a training model; S2, based on the light flux transferfunction matrix model, obtaining the required indoor illuminance through LED luminous flux calculation; S3, linearly superposing the illuminance of the n light sources, calculating the illuminance ofthe corresponding point, performing reverse approximation according to a successive approximation rule to obtain the luminous flux of the lamp, and obtaining the illuminance of the calculation point;and S4, calculating the illuminance needing to be compensated by utilizing the illuminance data, predicted by the BP neural network model, of the natural light at a plurality of point locations of the indoor working face. According to the method, under the condition that natural illumination is fully utilized in different seasons, the maximum balance among the three requirements of seeking energyconservation, saving and comfort is met through light supplement.
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Description

technical field

[0001] The invention belongs to the technical field of energy saving and environmental protection, in particular to the field of intelligent algorithms in green lighting, and relates to an indoor illumination estimation method based on BP neural network algorithm. Background technique

[0002] With the rapid development of my country's social economy, the electricity consumption of the society continues to rise. According to statistics, in my country, lighting accounts for 10% of household electricity consumption and more than 30% of commercial building electricity consumption. The power resource consumption of architectural lighting is one of the main components of my country's energy consumption. In order to improve the effective utilization of power resources, it is necessary to study greener lighting solutions.

[0003] The greenness of the lighting scheme is reflected in its meeting the three requirements of energy saving, economy and comfort. Energy ...

Claims

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