论文标题
英格兰东北部的每日中期概率预测
Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England
论文作者
论文摘要
在中期视野中对功耗的概率预测（几个月到一年）是能源部门的主要挑战。它在规划未来一代植物和传输网格中起着关键作用。我们提出了一个新模型，该模型将趋势和季节性特征纳入传统的时间序列分析和天气条件中，作为一种简约的机器学习方法中的阐释变量，称为高斯工艺。应用于最大能源供应商之一提供的日常功耗数据集，我们在长达一年的样本外密度预测中获得了有希望的结果，甚至使用一个小型数据集，只有两年的样本中数据。为了验证所达到的功耗概率预测的质量，我们将能源部门中常见的措施视为弹球损失和Winkler评分，并进行了有条件测试和无条件测试，这是Basel II协定引入后银行业的标准措施。
Probabilistic forecasting of power consumption in a middle-term horizon (months to a year) is a main challenge in the energy sector. It plays a key role in planning future generation plants and transmission grid. We propose a new model that incorporates trend and seasonality features as in traditional time-series analysis and weather conditions as explicative variables in a parsimonious machine learning approach, known as Gaussian Process. Applying to a daily power consumption dataset in North East England provided by one of the largest energy suppliers, we obtain promising results in Out-of-Sample density forecasts up to one year, even using a small dataset, with only a two-year In-Sample data. In order to verify the quality of the achieved power consumption probabilistic forecast we consider measures that are common in the energy sector as pinball loss and Winkler score and backtesting conditional and unconditional tests, standard in the banking sector after the introduction of Basel II Accords.
