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Short-term load forecasting for multiple buildings: A length sensitivity-based approach
- Source :
- Energy Reports, Vol 8, Iss , Pp 14274-14288 (2022)
- Publication Year :
- 2022
- Publisher :
- Elsevier, 2022.
-
Abstract
- With the rapid development of large-scale building energy monitoring platforms, it is of great significance to develop precise forecasting methods for buildings on a large scale to achieve better energy system design, system operation, energy management, and renewable energy integration in the grid. Traditionally, using all available historical data to train a data-driven model has been widely employed to ensure prediction performance because more historical information can be learned. However, this strategy may introduce more noise, especially for short-term load forecasting. Thus, this study proposes a novel approach for selectively utilizing building historical data to determine the amount of data that should be used to train the data-driven model. First, the CV(RMSE) curve of each building reflecting the relationship between training data length and forecasting accuracy is obtained using LightGBM. Second, clustering techniques such as k-means are used to identify buildings that are sensitive to the training data length based on CV(RMSE) curves. Finally, the optimal training data length for day-ahead forecasting is estimated for each building. The case study shows that approximately 20% of buildings in the Building Data Genome are labeled as length-sensitive buildings, and adopting appropriate training data lengths can reduce the prediction error of these buildings by up to 15%.
Details
- Language :
- English
- ISSN :
- 23524847
- Volume :
- 8
- Issue :
- 14274-14288
- Database :
- Directory of Open Access Journals
- Journal :
- Energy Reports
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.29fb0a5d5e5643868ff79bf9fbdb9490
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.egyr.2022.10.425