Benchmarking algorithms for resource allocation in smart buildings

The energy allocation at the building level is a complex decision making process. To cope with the uncertainties introduced by the user behavior, new energy-intensive technologies, and renewable energy sources, a real-time adaptation of the building energy management system is required. This paper presents a benchmark of energy resource optimization system for smart buildings, and examines different solution approaches, such as MiniMax Algorithm (MM), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (Q-PSO). These mathematical and heuristic optimization techniques are all able to find the optimal tradeoff between various resources and demands in the system. The proposed method and solution algorithms were tested on a simulated office building, which is powered by two sources of energy, one conventional, and one renewable, i.e. rooftop photovoltaics.