A method for detailed, short-term energy yield forecasting of photovoltaic installations
A method for detailed, short-term energy yield forecasting of photovoltaic installations
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DOI：
10.1016/j.renene.2018.06.058
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发表时间：
2019-01-01
期刊：
影响因子：
8.7
通讯作者：
Catthoor, F.
中科院分区：
文献类型：
--
作者：
Anagnostos, D.;Schmidt, T.;Catthoor, F.
全球转向可再生能源生产，加上电动汽车的预期渗透，云计算中心的能源使用量增加以及电网本身向“智能电网”的转型，需要在能源生产和管理的各个层面上提供新的解决方案。能源生产的预测，特别是将成为一个主要组成部分，在所有的时间和空间尺度的设计和操作，创造机会，优化控制的能量存储，本地能量交换等，为此，一种方法，用于创建详细和准确的能源产量预测光伏装置。基于天空成像仪的信息，并使用定制的神经网络，非常详细的能源产量预测产生的监测测试安装，视野长达15分钟，分辨率为1秒。热效应包括在计算中，并通过减少建模步骤来最大限度地减少误差传播。所描述的方法设法在预测技能方面超过最先进的模型高达39%，同时保持时间分辨率，使控制方案和能量交换在局部范围内。(C)2018爱思唯尔有限公司版权所有。
The global shift towards renewable energy production combined with the expected penetration of electric cars, increasing energy usage of cloud computing centers and the transformation of the electricity grid itself towards the "Smart Grid" requires novel solutions on all levels of energy production and management. Forecasting of energy production especially will become a major component for design and operation in all temporal and spatial scales, creating opportunities for optimized control of energy storage, local energy exchange etc. To this end, a method for the creation of detailed and accurate energy yield forecasts for PV installations is presented. Based on sky-imager information and using tailored neural networks, highly detailed energy yield forecasts are produced for a monitored test installation, for horizons up to 15 min and with a resolution of 1 s. Thermal effects are included in the calculations and error propagation is minimized by reducing the modeling steps. The described method manages to outperform state of the art models by up to 39% in forecast skill, while at the same time retaining temporal resolutions that enable control schemes and energy exchange in a local scale. (C) 2018 Elsevier Ltd. Alt rights reserved.