摘要：Detailed monitoring of loads can provide sufficient information about buildings and help in improving the operation of energy systems. Non-intrusive load monitoring (NILM) has become popular owing to its low cost and lack of disturbance in the occupied space. In this study, NILM methods based on artificial neural network (ANN) and random forest (RF) are proposed and compared to obtain the sub-loads of cooling loads. The cooling loads are disaggregated into four sub-loads: occupant load, equipment load, fresh air load, and building envelope load. Results showed both NILM methods could achieve load disaggregation accurately, but the RF-based NILM method performs better than that based on ANN. Moreover, the RF-based NILM method has mean relative errors (MREs) between 2.5% and 13.0%, while the ANN-based NILM method has MREs between 3.1% and 12.9%. Among the four sub-loads, the equipment load could be disaggregated with the highest accuracy. The detailed sub-loads obtained using the proposed NILM methods can guide building renovation and optimization design of building energy systems.
