期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2018
卷号：2233&2234
页码：624-628
出版社：Newswood and International Association of Engineers
摘要：The short term electric load forecasting
which is generally from one hour to one week is one of the
intelligent electric grid (smart grid), for control of stable
load supply hour-to-hour or day-to-day. The difficulty of
short time forecasting is that the trend of time series
usually change, and the non-adaptive auto-regressive
integrated moving average (ARIMA) could not fit
accurately. To solve that problem, conventional adaptive
ARIMA with constant forgetting factor that gives a
larger weight to more recent train data for dealing with
non-stationary change of stochastic disturbance. The
forgetting factor governs the recursive least squares
(RLS) algorithm. However, constant forgetting factor
usually result in over-fitting that increases forecasting
error. A new adaptive ARIMA is proposed in this paper
to improve the accuracy with lazy learning algorithm to
reduce over-fitting error.
