论文标题
使用高斯工艺回归对光伏发电的短期预测
Short-term prediction of photovoltaic power generation using Gaussian process regression
论文作者
论文摘要
光伏（PV）功率受到天气条件的影响，从而使PV系统产生的功率不确定。解决此问题将有助于提高电网的可靠性和成本效益，并有助于减少对化石燃料厂的依赖。本文的重点是评估英国高斯流程回归（GPR）中PV系统产生的能量的预测。高斯工艺回归是一种贝叶斯非参数模型，可以提供预测以及预测值的不确定性，这在高度不确定性的应用中非常有用。对于三个主要因素（训练期，天空区域覆盖率和内核模型选择），对该模型进行了48小时的短期预测，并对Sky Area的短期预测进行了四个小时的预测。我们还根据预测期内的云覆盖范围，仅作为预测指标的云覆盖范围来比较非常短期的预测。
Photovoltaic (PV) power is affected by weather conditions, making the power generated from the PV systems uncertain. Solving this problem would help improve the reliability and cost effectiveness of the grid, and could help reduce reliance on fossil fuel plants. The present paper focuses on evaluating predictions of the energy generated by PV systems in the United Kingdom Gaussian process regression (GPR). Gaussian process regression is a Bayesian non-parametric model that can provide predictions along with the uncertainty in the predicted value, which can be very useful in applications with a high degree of uncertainty. The model is evaluated for short-term forecasts of 48 hours against three main factors -- training period, sky area coverage and kernel model selection -- and for very short-term forecasts of four hours against sky area. We also compare very short-term forecasts in terms of cloud coverage within the prediction period and only initial cloud coverage as a predictor.
