Residential Energy-Saving Lighting Based on Bioinspired Algorithms

Abstract

Traditional residential lighting systems have the problem of high energy consumption. Based on artificial neural network (ANN), combined with particle swarm optimization algorithm, and genetic algorithm to optimize the initial weights and thresholds, an improved ANN prediction model for residential energy-saving lighting is proposed, and an actual residential lighting project is taken as an example to verify it. The results show that the proposed method can quickly predict the number of residential lighting lamps under the premise of meeting the standard illumination of residential lighting. The prediction accuracy can reach 98.45%, which has the characteristics of high prediction accuracy and small error. Compared with the ANN model and ANFIS model, the average relative error of the proposed prediction model is reduced by 2.29% and 0.87%, respectively, which has certain effectiveness and superiority. It provides a new idea for residential energy-saving lighting.