Policy Design for Controlling Set-Point Temperature of ACs in Shared Spaces of Buildings

Air conditioning systems are responsible for the major percentage of energy consumption in buildings. Shared spaces constitute considerable office space area, in which most office employees perform their meetings and daily tasks, and therefore the ACs in these areas have significant impact on the energy usage of the entire office building. The cost of this energy consumption, however, is not paid by the shared space users, and the AC's temperature set-point is not determined based on the users' preferences. This latter factor is compounded by the fact that different people may have different choices of temperature set-points and sensitivities to change of temperature. Therefore, it is a challenging task to design an office policy to decide on a particular set-point based on such a diverse preference set. As a result, users are not aware of the energy consumption in shared spaces, which may potentially increase the energy wastage and related cost of office buildings. In this context, this paper proposes an energy policy for an office shared space by exploiting an established temperature control mechanism. In particular, we choose meeting rooms in an office building as the test case and design a policy according to which each user of the room can give a preference on the temperature set-point and is paid for felt discomfort if the set-point is not fixed according to the given preference. On the other hand, users who enjoy the thermal comfort compensate the other users of the room. Thus, the policy enables the users to be cognizant and responsible for the payment on the energy consumption of the office space they are sharing, and at the same time ensures that the users are satisfied either via thermal comfort or through incentives. The policy is also shown to be beneficial for building management. Through experiment based case studies, we show the effectiveness of the proposed policy.Comment: Journal paper accepted in Energy & Buildings (Elsevier

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