天空图像分析与太阳能预测:卷积神经网络方法

Q4 Energy
A. Jakoplić, S. Vlahinić, B. Dobraš, D. Franković
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引用次数: 0

摘要

最近,可再生能源在生产单位能源结构中的份额稳步增加。可再生能源的不可预测性导致了规划、管理和控制电力能源系统(EES)的困难。减少不可预测的可再生能源的负面影响的方法之一是预测这些能源的可用性。对光伏发电厂产量的短期预测是将可再生能源更大程度地纳入EES的工具之一。为短期预测生产模型收集信息的一种方法是连续拍摄光伏电站上方的半球。通过对图像中包含的数据进行处理,得到与观测电厂当前输出功率相关的参数。本文提出了一种利用卷积神经网络分析电厂上空半球形天空图像来预测电厂当前输出功率的模型。估算目前的产量是开发短期太阳预报模型的关键一步。该模型是专门针对光伏电站开发的,能够实现较高的功率预测精度。通过对光伏电站发电量的估计,可以使用下一帧预测进行短期预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sky Image Analysis and Solar Power Forecasting: A Convolutional Neural Network Approach
Recently, the share of renewable sources in the energy mix of production units has been steadily increasing. The unpredictability of renewable sources leads to difficulties in planning, managing and controlling the electric energy system (EES). One of the ways to reduce the negative impact of unpredictable renewable sources is to predict the availability of these energy sources. Short-term forecasting of photovoltaic power plant production is one of the tools that enable greater integration of renewable energy sources into the EES. One way to gather information for the short-term forecast production model is to continuously photograph the hemisphere above the photovoltaic power plant. By processing the data contained within the images, parameters related to the current output power of the observed power plant are obtained. This paper presents a model that utilises a convolutional neural network to analyse images of the hemispherical sky above a power plant to predict the current output power of the power plant. Estimating current production is a crucial step in developing models for short-term solar forecasts. The model was specifically developed for photovoltaic power plants and is capable of achieving high accuracy in power prediction. The estimation of power production from photovoltaic power plants enables the use of next-frame prediction for short-term forecasting.
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来源期刊
Renewable Energy and Power Quality Journal
Renewable Energy and Power Quality Journal Energy-Energy Engineering and Power Technology
CiteScore
0.70
自引率
0.00%
发文量
147
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