Pattern Recognition for Electric Energy Consumption Prediction in a Laboratory Environment

Particle Swarm Optimization (PSO), a computational intelligence (CI) technique, is applied in a laboratory environment to recognize existence of a pattern between the net energy consumption by the electric loads in the building and the ambient temperature along with the occupancy state of the building; and use the detected pattern to predict energy consumption in the near-future. The electric loads under consideration include lighting and heating, ventilation and air conditioning (HVAC) units with intelligent monitoring and control capabilities using internet of things (IoT) devices and technologies. Having this prediction capability is extremely useful to ensure sufficient energy is generated to meet the demands of the electric loads at any time. This, in turn, reduces energy waste due to excess generation.