基于四种季节属性HWT模型的电力需求预测
Electricity Demand Forecasting Using HWT Model with Four-Fold Seasonality
摘要: 基于日、周和年三种时间属性的滑动平均模型预测方法已经在电力需求短期预测方面得到了广泛应用。在本文中，我们研究了一种新的短期电力需求的模型和预测方法，即在日、周和年时间属性的基础上增加了一个月周期，并将这种新的周期应用到HWT模型中得到基于四种时间周期属性的预测算法，通过新加坡市场电力需求预测模型仿真得知，新的包含月周期的预测方法准确性更高。
Abstract: Seasonality methods have been developed to model the intraday, intraweek and intrayear seasonal cycles of the electricity load data in one-day ahead electricity demand forecasting. In this paper, we investigate the short-term modeling and forecasting of electricity demand where an intramonth cycle has also been discovered. Thus based on the intramonth cycle, a new mathematical modeling scheme is developed for HWT exponential smoothing model to accommodate the intramonth seasonal cycle and by using six years of Singapore data. We show that fourfold seasonal method outperforms the triple seasonal method in Singapore.
文章引用：吴文贤. 基于四种季节属性HWT模型的电力需求预测[J]. 智能电网, 2018, 8(6): 547-554. https://doi.org/10.12677/SG.2018.86060

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