A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant

Wang, Fei; Lu, Xiaoxing; Mei, Shengwei; Su, Ying; Zhen, Zhao; Zou, Zongshu; Zhang, Xuemin; Yin, Rui; Duić, Neven; Shafie‐khah, Miadreza; Catalão, João P.S.

doi:10.1016/j.energy.2021.121946

Cited by 82 publications

(19 citation statements)

References 41 publications

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“…Thus, naturally, the exploitation and proposal of different strategies can be found in various spatio-temporal solar forecasting works. In this line, many works implement input selection methods based on user-defined criteria, such as distance (e.g., [89,103,129]) or the degree of correlation (e.g., [41,105,106]) to the target site, the importance of features [26,106], usually derived from a model, or according to local wind patterns (e.g., [14,40,85]).…”

Section: Data Sourcesmentioning

confidence: 99%

Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates

et al. 2022

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To better forecast solar variability, spatio-temporal methods exploit spatially distributed solar time series, seeking to improve forecasting accuracy by including neighboring solar information. This review work is, to the authors’ understanding, the first to offer a compendium of references published since 2011 on such approaches for global horizontal irradiance and photovoltaic generation. The identified bibliography was categorized according to different parameters (method, data sources, baselines, performance metrics, forecasting horizon), and associated statistics were explored. Lastly, general findings are outlined, and suggestions for future research are provided based on the identification of less explored methods and data sources.

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Section: Data Sourcesmentioning

confidence: 99%

Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates

et al. 2022

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“…A lasso-based short-term forecasting model for a PV system is proposed in Zhao et al (2019) and Wang et al (2022). It constructs a penalty function that allows it to compress some regression coefficients to obtain a refined model.…”

Section: Forecasted Reserve Controlmentioning

confidence: 99%

Frequency regulation in adaptive virtual inertia and power reserve control with high PV penetration by probabilistic forecasting

Chang

Du²,

Chen³

et al. 2022

Front. Energy Res.

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The large-scale deployment of sustainable energy sources has become a mandatory goal to reduce pollution from electricity production. As photovoltaic (PV) plants replace conventional synchronous generators (SGs), their significant inherent rotational inertia characteristics are reduced. The high penetration of PV results in reduced system inertia, leading to system frequency instability. Virtual inertial control (VIC) technology has attracted increasing interest because of its ability to mimic inertia. Adoption of the energy storage system (ESS) is hindered by the high cost, although it can be used to provide virtual inertia. The determined forecast gives PVs the ability to reserve power before shading and compensate the power when a system power drop occurs, which can increase system inertia. Nevertheless, it has forecast errors and energy waste in a stable state. To improve the stability of the microgrid and improve the ESS efficiency, this study proposes an adaptive forecasting-based (AFB) VIC method using probabilistic forecasts. The adaptive power reserve and virtual inertia control are proposed to reduce energy waste and increase system inertia. The simulation results reveal that the proposed method has adaptive system inertia to reduce the reserved power, required ESS power capacity, and battery aging.

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“…Also, the authors of [4] has considered sky imagers for solar irradiance forecast up to 4 hours ahead. In [9], the authors exploited satellite images in ultra-short-term forecasting of solar photovoltaic power using algorithms of support vector machine and gradient boosting decision trees. Similarly, in [10], the authors proposed a way of irradiance forecasting based on sky images captured by two sky cameras.…”

Section: A Related Workmentioning

confidence: 99%

Sky Imager Data Reduction Using Autoencoder and Internet of Things Computing

et al. 2022

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Forecasting the output power of renewable energy sources is of an utmost importance for maintaining the stability of the electricity grid and ensuring uninterrupted service. Internet-of-Things (IoT) devices equipped with sky imagers are often used to estimate solar irradiance whose value is essential for forecasting solar energy. This paper considers distributed sky imagers whose data are transmitted through the Internet to a cloud for possible storage, forecasting, and/or performing other signal processing operations. The data comprise a collection of images, and this makes their streaming to the cloud a burden, i.e., they require a large bandwidth. In an attempt to deal with that, an IoT system is proposed in this paper such that it collects, compresses, transmits, and reconstructs those images with as little bandwidth as possible. More specifically, an IoT device multiplexes the collected images and compresses them using a deep learning algorithm, the encoder of a convolutional autoencoder (CAE) in particular. It, then, transmits the compressed images to a cloud server where they could be decompressed with high-fidelity using the decoder of the CAE. As the core to the proposed system, the performance of the CAE algorithm is evaluated using a publicly available dataset of sky images. Results indicate that the images can be compressed to 2% of their original size (i.e., compression ratio of 98%) and reconstructed again with high fidelity, i.e., average structural similarity index measure (SSIM) of 99%. Hardware implementation of the CAE algorithm has also been attempted using a Raspberry Pi platform representing the IoT device with an average processing speed of 6.6681 seconds per frame. This speed well meets practical requirements given the low frame-rate of sky imagers.INDEX TERMS Convolutional auto-encoder, image compression, sky imager, raspberry pi, Internet of Things.

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A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant

Cited by 82 publications

References 41 publications

Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates

Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates

Frequency regulation in adaptive virtual inertia and power reserve control with high PV penetration by probabilistic forecasting

Sky Imager Data Reduction Using Autoencoder and Internet of Things Computing

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