Energy optimization in smart urban buildings using bio-inspired ant colony optimization

In this paper, a smart home energy management system is proposed to improve the efficiency of the electricity infrastructure of residential buildings. To solve the scheduling problem of a smart building, we propose bacterial foraging ant colony optimization (HB-ACO). The primary objective of scheduling is to shift load from on-peak hours to off-peak hours to reduce electricity cost and peak-to-average ratio. A comparison of these algorithms is also presented in terms of performance parameters, electricity cost, reduction of PAR, and user comfort in terms of waiting time. The proposed techniques are evaluated using two pricing schemes: (1) time of use and (2) critical peak pricing. Moreover, coordination among home appliances is presented for real-time scheduling. We represent this as a knapsack problem and solve it through ant colony optimization algorithm. The HB-ACO shows better performance than ACO and BFA in reducing electricity cost, PAR, and increased user comfort, which is evident from the simulation results.

Automated summary

This paper proposes a smart home energy management system using the HB-ACO algorithm to solve the scheduling problem in smart buildings. By shifting load from on-peak hours to off-peak hours, the system aims to reduce electricity cost and peak-to-average ratio. The HB-ACO algorithm shows better performance than other algorithms in terms of reducing electricity cost, PAR, and improving user comfort, as demonstrated by simulation results.