Buildings consume quite a lot of energy; hence, the issue of building energy efficiency has attracted a great deal of attention in recent years. A key factor in achieving this objective is occupancy information that directly impacts on energy-related building control systems. In this paper, we leverage on environmental sensors that are nonintrusive and cost-effective for building occupancy estimation. Our result relies on feature engineering and learning. The conventional feature engineering requires one to manually extract relevant features without a clear guideline. This blind feature extraction is labor intensive and may miss some significant implicit features. To address this issue, we propose a convolutional deep bidirectional long short-term memory (CDBLSTM) approach that contains a convolutional network and a deep structure to automatically learn significant features from the sensory data without human intervention. Moreover, the long short-term memory networks are able to capture temporal dependencies in the data and the bidirectional structure can take the past and future contexts into consideration for the final identification of occupancy. We have conducted real experiments to evaluate the performance of our proposed CDBLSTM approach. Instead of estimating the exact number of occupants, we attempt to identify the range of occupants, i.e., zero, low, medium, and high, which is adequate for most of building control systems. The experimental results indicate the effectiveness of our proposed approach compared with the state-of-the-art methods.

Fault Tolerant Control (FTC) systems are crucial in industry to ensure safe and reliable operation, especially of motor drives. This paper proposes the use of multiple controllers for a FTC system of an induction motor drive, selected based on a switching mechanism. The system switches between sensor vector control, sensorless vector control, closed-loop voltage by frequency (V/f) control and open loop V/f control. Vector control offers high performance, while V/f is a simple, low cost strategy with high speed and satisfactory performance. The faults dealt with are speed sensor failures, stator winding open circuits, shorts and minimum voltage faults. In the event of compound faults, a protection unit halts motor operation. The faults are detected using a wavelet index. For the sensorless vector control, a novel Boosted Model Reference Adaptive System (BMRAS) to estimate the motor speed is presented, which reduces tuning time. Both simulation results and experimental results with an induction motor drive show the scheme to be a fast and effective one for fault detection, while the control methods transition smoothly and ensure the effectiveness of the FTC system. The system is also shown to be flexible, reverting rapidly back to the dominant controller if the motor returns to a healthy state.