Abstract
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.
Highlights
Buildings can account for 50% of the total energy consumption of society and 30% of greenhouse gas emissions [1]
The results showed that the data analytics method can accurately monitor the energy consumption of the lighting system, and the relative difference between the disaggregation results and monitoring data was less than 5%
The results indicate that the approach can disaggregate the total energy consumption ac
Summary
Buildings can account for 50% of the total energy consumption of society and 30% of greenhouse gas emissions [1]. Decreasing the energy consumption of buildings plays a key role in sustainable development. The energy consumption mainly includes embodied energy and operational energy. The embodied energy consists of energy used in the process of manufacturing building material, constructing buildings, and destructing buildings [2]. The operational energy often results from the heating, ventilation, and air conditioning (HVAC) system; lighting system; plug-in system; and elevator system [3], which accounts for 80–90% of the life-cycle energy consumption [4]. By reducing the operational energy, life-cycle energy can decline obviously. For existing buildings in operation, performance evaluation and renovation projects play an important role in reducing operational energy consumption and carbon emission
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