论文标题
神经网络中期概率预测日常功耗
Neural Network Middle-Term Probabilistic Forecasting of Daily Power Consumption
论文作者
论文摘要
中期视野（几个月至一年）的功耗预测是能源部门的主要挑战，特别是当考虑概率预测时。我们提出了一种新的建模方法，该方法将趋势，季节性和天气状况融合在一起，作为具有自回归特征的浅神经网络中的阐释变量。我们在一年测试集中的密度预测中获得了出色的结果，该测试将其应用于美国新工程师的日常功耗。
Middle-term horizon (months to a year) power consumption prediction is a main challenge in the energy sector, in particular when probabilistic forecasting is considered. We propose a new modelling approach that incorporates trend, seasonality and weather conditions, as explicative variables in a shallow Neural Network with an autoregressive feature. We obtain excellent results for density forecast on the one-year test set applying it to the daily power consumption in New England U.S.A.. The quality of the achieved power consumption probabilistic forecasting has been verified, on the one hand, comparing the results to other standard models for density forecasting and, on the other hand, considering measures that are frequently used in the energy sector as pinball loss and CI backtesting.
