论文标题
修改了单变量时间序列太阳辐照度预测的自动回归技术
Modified Auto Regressive Technique for Univariate Time Series Prediction of Solar Irradiance
论文作者
论文摘要
可再生资源的集成在发电中增加了，以减少化石燃料的使用并减轻对环境的不利影响。但是，由于天气模式的高度依赖性，因此可更可再生能源在本质上是随机的。这种不确定性大大降低了太阳能电池板整合的好处，并增加了由于较大的能源储备的要求而增加了运营成本。为了解决这个问题，改进的自动回归模型，卷积神经网络和长期的短期记忆神经网络，可以准确预测太阳辐照度。通过多个验证的误差指标，将提出的技术相互比较。修改后的自动回归模型在10分钟，30分钟和1小时的预测范围内的平均绝对百分比误差为14.2％，19.9％和22.4％。因此，提出了修改的自动回归模型是最强大的方法，它吸收了太阳预测问题的艺术神经网络的状态。
The integration of renewable resources has increased in power generation as a means to reduce the fossil fuel usage and mitigate its adverse effects on the environment. However, renewables like solar energy are stochastic in nature due to its high dependency on weather patterns. This uncertainty vastly diminishes the benefit of solar panel integration and increases the operating costs due to larger energy reserve requirement. To address this issue, a Modified Auto Regressive model, a Convolutional Neural Network and a Long Short Term Memory neural network that can accurately predict the solar irradiance are proposed. The proposed techniques are compared against each other by means of multiple error metrics of validation. The Modified Auto Regressive model has a mean absolute percentage error of 14.2%, 19.9% and 22.4% for 10 minute, 30 minute and 1 hour prediction horizons. Therefore, the Modified Auto Regressive model is proposed as the most robust method, assimilating the state of the art neural networks for the solar forecasting problem.
