Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization

Table 1

The input variables of the ANN for electrical energy consumption forecasting.

Category	Variables
First	𝑁1,1∼𝑁3,3, 𝑃𝐴 (the total rated power of electric apparatus of all equipment), 𝑃𝑀 (the total rated motor power of all equipment), 𝑇𝐷 (the expected total fault detection time of all equipment), 𝑇𝐶 (the expected total fault correction time of all equipment), 𝑇𝑅 (the expected total spare parts replacement time of all equipment)
Second	𝑁𝑊 (the number of workshops), 𝑁𝐿 (the number of maintenance lines), 𝐻𝑀1 (the available machine-hours of principle maintenance machines), 𝐻𝑀2 (the available machine-hours of auxiliary maintenance machines), 𝐻𝑊1 (the available working hours of senior maintenance workers), 𝐻𝑊2 (the available working hours of common maintenance workers), 𝑇𝐿 (the lower limit of working temperature), 𝑇𝑈 (the upper limit of working temperature), 𝑇𝐸 (the average environment temperature)