access icon free Experimental testing of a random neural network smart controller using a single zone test chamber

Monitoring and analysis of energy use and indoor environmental conditions is an urgent need in large buildings to respond to changing conditions in an efficient manner. Correct estimation of occupancy will further improve energy performance. In this work, a smart controller for maintaining a comfortable environment using multiple random neural networks (RNNs) has been developed. The implementation of RNN-based controller is demonstrated to be more efficient on hardware and requires less memory compared to both artificial neural networks and model predictive controllers. This controller estimates the number of room occupants by using the information from wireless sensor nodes placed in the Heating, Ventilation and Air Conditioning (HVAC) duct and the room. For an occupied room, the controller can switch between thermal comfort mode (based on predicted mean vote set points) and user defined mode (i.e. occupant defined set points for heating/cooling/ventilation). Furthermore, the hybrid particle swarm optimisation with sequential quadratic programming training algorithms are used (for the first time to the best of the authors' knowledge) for training the RNN and results show that this algorithm outperforms the widely used gradient descent algorithm for RNN. The results show that occupancy estimation by smart controller is 83.08% accurate.

Inspec keywords: indoor environment; gradient methods; neurocontrollers; HVAC; wireless sensor networks; energy conservation; intelligent control; particle swarm optimisation; building management systems; ergonomics; quadratic programming

Other keywords: hybrid particle swarm optimisation; indoor environment; comfortable environment; predicted mean vote-based set points; building energy control system; heating-ventilation-air conditioning; random neural network smart controller; wireless sensor nodes; RNN-based controller; experimental testing; model predictive controllers; sequential quadratic programming training algorithms; energy usage analysis; gradient descent algorithm; artificial neural networks; single zone test chamber; thermal comfort mode; energy usage monitoring; occupancy estimation; environmental conditions; HVAC duct

Subjects: Space heating; Control of heat systems; Interpolation and function approximation (numerical analysis); Air conditioning; Wireless sensor networks; Optimisation techniques; Fuzzy control; Control engineering computing; Automated buildings; Neurocontrol; Interpolation and function approximation (numerical analysis); Optimisation techniques

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