Dynamic Feature Selection for Solar Irradiance Forecasting Based on Deep Reinforcement Learning
Dynamic Feature Selection for Solar Irradiance Forecasting Based on Deep Reinforcement Learning
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DOI：
10.1109/tia.2022.3206731
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发表时间：
2023-01-01
影响因子：
4.4
通讯作者：
Xu, Chongfang
中科院分区：
文献类型：
--
作者：
Lyu, Cheng;Eftekharnejad, Sara;Xu, Chongfang
要实现准确的太阳能发电量预测，通常需要大量的数据。然而，并不是所有类型的数据都是一致可用的。各种研究努力通过开发确定与预测太阳能发电量最相关的特征的方法来应对这一挑战。然而，最优特征随天气模式的不同而不同，因此不可能为所有天气模式选择一组固定的最佳特征。这项研究开发了一种新的框架，使用动态变化的最优特征来准确地预测太阳辐射。开发的模型首先将特征提取与聚类技术相结合，以从数据集中识别具有代表性的天气数据。其次，利用深度强化学习(DRL)，开发了一种新的特征选择方法，以获得从代表性数据中准确预测太阳辐射所需的最少特征。得益于DRL的无模型特性，该方法能够适应不同的天气条件，并动态改变所选择的特征。使用真实世界数据的案例研究表明，开发的模型显著减少了针对不同天气模式进行准确辐照度预报所需的数据量。
A large volume of data is typically needed to achieve an accurate solar generation prediction. However, not all types of data are consistently available. Various research efforts have addressed this challenge by developing methods that identify the most relevant features for predicting solar generation. However, the optimal features vary with different weather patterns, making it impossible to select a fixed set of optimal features for all weather patterns. This study develops a new framework to accurately predict solar irradiance using dynamically changing optimal features. The developed model first incorporates feature extraction with clustering techniques to identify representative weather data from a dataset. Next, using deep reinforcement learning (DRL), a new feature selection method is developed to yield the minimum features required to accurately forecast solar irradiance from representative data. Benefiting from the model-free nature of DRL, the developed method is adaptive to various weather conditions, and dynamically alters the selected features. Case studies using real-world data have shown that the developed model significantly reduces the volume of data required for accurate irradiance forecasting for different weather patterns.