Modeling and Short-Term Forecasting of New South Wales Electricity System Load
用贝叶斯半参数回归方法对日内电力负荷建模并做短期预测，识别日周期、周周期和温度敏感成分，通过蒙特卡洛样本获取预测分布，适用于电力市场运营。
This article employs Bayesian semiparametric regression methodology to model intraday electricity load data and obtain short-term load forecasts. The role of such forecasts in the New South Wales wholesale electricity market is discussed and the method applied to New South Wales system load data. The semiparametric regression model used identifies daily periodic, weekly periodic, and temperature-sensitive components of load. Each component is decomposed as a linear combination of basis functions, with a nonzero probability mass that the corresponding coefficients are exactly zero. Three possible models for the errors are also considered, including independent, autoregressive, and first-differenced autoregressive models. A moving window of data is used to overcome the slow time-varying nature of the temperature and periodic effects. The entire model is estimated using a Bayesian Markov chain Monte Carlo approach, and forecasts are obtained using a Monte Carlo sample from the joint predictive distribution of future system load. It is demonstrated how accurate temperature forecasts can result in accurate intraday system load forecasts for even quite long forecast horizons.