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This paper studies the magnet shape optimization of a five-phase surface-mounted permanent magnet (PM) machine to improve its torque performance. First, a sinusoidal PM with third and fifth harmonics (sine + third + fifth PM) is presented to enhance the torque. An equivalent PM method is then proposed to avoid the complicated interface conditions between PM and the air gap, making the derivation of torques with magnet shapes using 2-D analytical models possible. Besides, the influence of PM edge thickness on torque improvement is considered. Moreover, under the condition that the copper losses of machines with different magnet shapes are kept unchanged, optimal harmonics of sinusoidal PM with third harmonic (sine + third PM) and sine + third + fifth PM are analytically derived. Finite element (FE) analysis is performed to validate the equivalent PM method and analytical results. Finally, machines with sine + third PM, sine + third + fifth PM and conventional (without shaping) PM are comparatively studied using nonlinear FE analysis and the experimental results of machine with sine + third + fifth PM are presented, verifying the effectiveness of the analytical optimization of magnet shape.

Load monitoring can help users learn end-use energy consumption so that specific energy-saving actions can be taken to reduce the energy consumption of buildings. Nonintrusive monitoring (NIM) is preferred because of its low cost and nondisturbance of occupied space. In this study, a NIM method based on random forest was proposed to determine the energy consumption of building subsystems from the building-level energy consumption: the heating, ventilation and air conditioning system; lighting system; plug-in system; and elevator system. Three feature selection methods were used and compared to achieve accurate NIM based on weather parameters, wavelet analysis, and principal component analysis. The implementation of the proposed method in an office building showed that it can obtain the subloads accurately, with root-mean-square errors of less than 46.4 kW and mean relative errors of less than 12.7%. The method based on weather parameters can provide the most accurate results. The proposed method can help improve the energy efficiency of building service systems during the operation or renovation stage.