Deep Learning-Based Multi-Step Solar Forecasting for PV Ramp-Rate Control Using Sky Images

Wen, Haoran, Du, Yang, Chen, Xiaoyang, Lim, Enggee ORCID: 0000-0003-0199-7386, Wen, Huiqing, Jiang, Lin ORCID: 0000-0001-6531-2791 and Xiang, Wei
(2020) Deep Learning-Based Multi-Step Solar Forecasting for PV Ramp-Rate Control Using Sky Images. IEEE Transactions on Industrial Informatics, 17 (2). p. 1.

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Solar forecasting is one of the most promising approaches to address the intermittent photovoltaic (PV) power generation by providing predictions before upcoming ramp events. In this article, a novel multistep forecasting (MSF) scheme is proposed for PV power ramp-rate control (PRRC). This method utilizes an ensemble of deep ConvNets without additional time series models (e.g., recurrent neural network (RNN) or long short-term memory) and exogenous variables, thus more suitable for industrial applications. The MSF strategy can make multiple predictions in comparison with a single forecasting point produced by a conventional method while maintaining the same high temporal resolution. Besides, stacked sky images that integrate temporal-spatial information of cloud motions are used to further improve the forecasting performance. The results demonstrate a favorable forecasting accuracy in comparison to the existing forecasting models with the highest skill score of 17.7%. In the PRRC application, the MSF-based PRRC can detect more ramp-rates violations with a higher control rate of 98.9% compared with the conventional forecasting-based control. Thus, the PV generation can be effectively smoothed with less energy curtailment on both clear and cloudy days using the proposed approach.

Item Type: Article
Uncontrolled Keywords: Forecasting, Predictive models, Image resolution, Clouds, Feature extraction, Cloud computing, Informatics, Deep learning (DL), multistep forecasting (MSF), power ramp-rate control (PRRC), stacked sky images (SIs), solar forecasting
Depositing User: Symplectic Admin
Date Deposited: 01 May 2020 09:59
Last Modified: 18 Jan 2023 23:53
DOI: 10.1109/tii.2020.2987916
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