论文标题
通过合奏方法改善太阳能和光伏电源预测
Improving Solar and PV Power Prediction with Ensemble Methods
论文作者
论文摘要
估计可再生能源的生成能力对于计划操作以及需求平衡和电力质量一般重要。本文解决了估计短期（前3小时）和中期（提前为期1天）产生的光伏植物的问题的问题。首先，通过时间序列模型的不同设置进行了日常太阳辐射预测变量的设计，并使用集合方法将其与天气预报服务的组合进行了研究。在此阶段还采用了支持向量机方法来集群数据。其次，在类似的整体框架下，研究了生成的功率预测。然后，在低成本的，嵌入式的Mini PC模块Raspberry Pi 3上实施了整个生成的功率和太阳辐射预测任务。作为应用程序，该预测是在典型的微电气设置的控制系统中，重点是能源管理问题。本文还评估了生成的功率预测质量对控制器性能的影响。
Estimation of the generated power of renewable energy resources is in general important for planning operations as well as demand balance and power quality. This paper addresses the problem of the estimation of the short-term (3-hour ahead) and medium-term (1-day ahead) generated power of a photovoltaic plant. Firstly, the design of day-ahead solar radiation predictors is investigated with different setups of time series models, and with their combinations with the weather forecast services using ensemble methods. Support Vector Machine methods are also adopted in this stage, to cluster data. Secondly, under a similar ensemble framework, the generated power prediction is investigated. The whole generated power and solar radiation prediction tasks are then implemented on a low-cost, embedded mini PC module Raspberry Pi 3. As an application, the prediction is employed in the control system of a typical microgrid settings focusing on energy management problem. The impact of the quality of generated power prediction on the performance of the controller is also evaluated in this paper.
