Short-term Load Forecasting at Different Aggregation Levels with
Predictability Analysis
release_drf2t3wcsrhmnigsghdbqlszeu
by
Yayu Peng, Yishen Wang, Xiao Lu, Haifeng Li, Di Shi, Zhiwei Wang, Jie
Li
2019
Abstract
Short-term load forecasting (STLF) is essential for the reliable and economic
operation of power systems. Though many STLF methods were proposed over the
past decades, most of them focused on loads at high aggregation levels only.
Thus, low-aggregation load forecast still requires further research and
development. Compared with the substation or city level loads, individual loads
are typically more volatile and much more challenging to forecast. To further
address this issue, this paper first discusses the characteristics of
small-and-medium enterprise (SME) and residential loads at different
aggregation levels and quantifies their predictability with approximate
entropy. Various STLF techniques, from the conventional linear regression to
state-of-the-art deep learning, are implemented for a detailed comparative
analysis to verify the forecasting performances as well as the predictability
using an Irish smart meter dataset. In addition, the paper also investigates
how using data processing improves individual-level residential load
forecasting with low predictability. Effectiveness of the discussed method is
validated with numerical results.
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