A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest
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.
A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest
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.
A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest
Zaixun Ling (author) / Qian Tao (author) / Jingwen Zheng (author) / Ping Xiong (author) / Manjia Liu (author) / Ziwei Xiao (author) / Wenjie Gang (author)
2021
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings
| Online Contents | 2011
User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings
| British Library Online Contents | 2011
User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings
| British Library Conference Proceedings | 2011
Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning
| DOAJ | 2021
Live Load Effects in Office Buildings
| NTIS | 1973