Activity Based Smart Lighting Control For Energy Efficient Building By Neural Network Model

• Main Authors: Hidayat Ivan, Faridah, Sesotya Utami Sentagi
• Format: Article
• Language: English
• Published: EDP Sciences 2018-01-01
• Series: E3S Web of Conferences
• Online Access: https://doi.org/10.1051/e3sconf/20184301017

The goal of this research is to attain a smart control algorithm that can be used in a lighting system based on the activities of a building's occupants, using the neutral network model method. The study case of this research is the activities inside the Asrama Mahasiswa Kinanti UGM building. Control algorithm was built based on qualitative data from the occupants of the building, which were more or less the daily activities of the occupants. The results of the qualitative data will be essential in choosing a sensor and its placement. Several scenarios of activities represented by the combination of sensors' outputs are used as the control system input. The optimum illumination of the lighting system for these scenarios was produced through simulation using DIALux. An artificial neural network model was then developed and used as the smart control algorithm. Input for the neural network is the combination of sensor output and illumination output for each scenarios, given the simulation results. Based on the qualitative data acquired through a survey of the occupants' activities, the design of the lighting control system requires a system that uses occupancy sensors, weight sensors, photoelectric sensors, and photo sensors. The various positions and activities being done by the occupants are represented by the sensors output. A manual remote will be used to adjust the sensors regarding details that cannot be specifically detected. Ongoing specific activities inside the inhabited room gives off a system output. This scenario portrays the lighting conditions of the room, which includes the number of lights that are turned on or turned off. A smart control algorithm was developed using the backpropagation neural network model with 10 neuron inputs, the first hidden layer with 20 neurons, second hidden layer with 20 neurons, whilst the output layer has 5 neurons. The activated function for the first hidden layer is tan-sigmoid, for the second hidden layer is log-sigmoid, and the output layer is using pure linear. The training function uses trainlm. The MSE system's value is 2.72 x 10-8 with a larger R total value, which is 0.99892.