Journal article
System identification and data fusion for on-line adaptive energy forecasting in virtual and real commercial buildings
Energy and buildings, v 129, pp 227-237
01 Oct 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
•Developed a novel real-time online adaptive energy forecasting methodology using system identification and data fusion.•Proposed a modeling reforming method to reconstruct state space models from Markov parameters.•Demonstrated the real field application of Kalman filter for parameter adaption and state estimation.•Validated the proposed framework in a virtual and a real field commercial building.•Achieved over 90% forecasting accuracy in both a virtual building and a real building.
Accurate, computationally efficient, and cost-effective energy forecasting models are essential for model based control. Existing studies in model based control have mostly been focusing on developing energy forecasting models using simplified physics based or data driven models. However, creating and identification the simplified physics model are often challenging, which requires expert knowledge for model simplification and significant engineering efforts for model training. In addition, the accuracy and robustness of data driven models are always bounded by the training data. To this end, developing high fidelity energy forecasting models with less engineering effort and good performance is still an urgent task. Although the previous studies from the authors have shown great promises in a system identification model and outperformed other data-driven and grey box models, they still have large errors at the special operation situations. Therefore, this paper investigates a novel methodology to develop energy estimation models for on-line building control and optimization using an integrated system identification and data fusion approach. The data fusion approach is able to adapt the forecasting model under the special operation situations based on the real measurements. An eigensystem realization algorithm based model reformation method is developed to convert the system identification models into state space models. Kalman filter based data fusion techniques are then implemented on the state space models to improve the model accuracy and robustness. The developed methodology are evaluated using data from a virtual building (simulated) and a real small size commercial building. Three different data fusion intervals: 15, 30, and 60min, have been tested. The overall building energy estimation accuracy from this proposed methodology can reach to above 95% in the virtual building and around 90% in the real building. The results also show that the shorter data fusion interval used, the higher accuracy can be achieved.
Metrics
Details
- Title
- System identification and data fusion for on-line adaptive energy forecasting in virtual and real commercial buildings
- Creators
- Xiwang Li - Center for Green Building and Cities, Graduate School of Design, Harvard University, Cambridge, MA 02138, USAJin Wen - Drexel University
- Publication Details
- Energy and buildings, v 129, pp 227-237
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000383811100020
- Scopus ID
- 2-s2.0-84982823923
- Other Identifier
- 991019168764304721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Construction & Building Technology
- Energy & Fuels
- Engineering, Civil