Cooling load disaggregation using a NILM method based on random forest for smart buildings

Abstract Accurate load monitoring can provide detailed information for users to improve the energy efficiency of buildings. Non-intrusive load monitoring (NILM) has become popular in smart buildings because of its low cost and reasonable privacy. In this paper, a non-intrusive monitoring method for the cooling load of smart buildings is proposed based on random forest. The total building cooling load is disaggregated into four subloads, and two approaches are used to realize the NILM based on the direct or indirect cooling load using a Fourier transform. The proposed method is implemented in an office building, and results show the method can realize cooling load disaggregation accurately. The root-mean-square errors and mean relative errors of the four subloads between the NILM loads and reference loads are less than 51.9 kW and 19.1%. Among the four subloads, the equipment load can be disaggregated with the highest accuracy. Approach I is recommended because of its higher accuracy. The NILM method is optimized in terms of the estimator number, maximum depth, feature number, minimum samples for a split, minimum sample leaf, and size of training samples. The performance of the optimized NILM models is improved with RMSEs and MREs less than 48.3 kW and 6.4%.