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

机译：使用单区域测试室对随机神经网络智能控制器进行实验测试

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摘要

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.
机译：大型建筑物中迫切需要对能源使用和室内环境条件进行监视和分析，以有效地应对不断变化的条件。正确估算占用率将进一步提高能源效率。在这项工作中，已经开发了一种使用多个随机神经网络（RNN）来维持舒适环境的智能控制器。与人工神经网络和模型预测控制器相比，基于RNN的控制器的实现在硬件上效率更高，并且所需内存更少。该控制器通过使用来自放置在加热，通风和空调（HVAC）管道和房间中的无线传感器节点的信息来估计房间的人数。对于一个占用的房间，控制器可以在热舒适模式（基于预测的平均投票设定点）和用户定义的模式（即，供暖/制冷/通风的乘客定义的设定点）之间切换。此外，使用具有连续二次规划训练算法的混合粒子群优化算法（据作者所知，这是第一次）用于训练RNN，结果表明该算法优于RNN广泛使用的梯度下降算法。结果表明，智能控制器的占用率估算准确率为83.08％。

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